SYSTEM AND METHODS TO CONFIGURE A PROFILE TO RANK SEARCH RESULTS

Abstract

A method and system to configure a profile to rank search results are provided. A profile that includes behavior information organized as a plurality of components is generated. Additional behavior information is received from a user. At least one component of the plurality of components is updated using the additional behavior information received from the user. A search query is received and search results are generated based on the search query. Search results are ranked based on the updated profile.

Description

PRIORITY

This application claims the priority benefit of U.S. Provisional Application No. 61/675,793, filed Jul. 25, 2012, which is incorporated herein by reference in its entirety.

A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. The following notice applies to the software and data as described below and in the drawings that form a part of this document: Copyright eBay, Inc. 2012, All Rights Reserved.

TECHNICAL FIELD

The present application relates generally to the technical field of processing and, in one specific example, to configure a profile to rank search results.

BACKGROUND

A user may generate a profile to rank search results generated from a search query. In order to change components of the profile, a separate new profile may be generated with the components changed. The new profile may then be used to rank the search results generated from the search query.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which:

FIG. 1 is a network diagram depicting a client-server system, where various embodiments embodiment may be implemented;

FIG. 2 is a block diagram of a profile configuration system, according to various embodiments;

FIG. 3 is a flowchart of a method to update a profile and rank search results using the updated profile, according to various embodiments;

FIG. 4 is a block diagram of a profile and additional behavior information being used to update the profile, according to various embodiments;

FIG. 5 is a block diagram of an updated profile; according to various embodiments;

FIG. 6 is a diagram depicting a component of a profile, according to various embodiments;

FIG. 7 is a diagram depicting a component of a profile, according to various embodiments;

FIG. 8 is a diagram depicting a component of a profile, according to various embodiments; and

FIG. 9 is a diagram depicting a representation of a machine in the example form of a computer system, according to various embodiments.

DETAILED DESCRIPTION

Example methods and systems to configure a profile to rank search results are described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art that the present invention may be practiced without these specific details.

A user may generate a profile to rank search results generated from a search query. The profile may be comprised of several components. To change the ranking of search results, the user may modify a component of the profile without having to create a new search profile. The user may send information to the system and have the information replace a component of the profile. Alternatively, the user may send information to be added to the profile. The modified profile may then be used to rank the search results.

FIG. 1 illustrates a system 10 to build and utilize a search infrastructure, according to an embodiment. The system 10 may include an information storage and retrieval platform 11 that is communicatively coupled over a network (e.g., Internet) (not shown) to a client machine 12 and a client machine 33.

Illustrated on the top left is an operation A that describes a first user operating the client machine 12 to interact with an application server 14 to store or update a document 16 in a database 18; illustrated in the middle are operations B, C, D, E that describe retrieving and transforming the contents of the database 18, storing the transformed contents in a database 20 that is time-stamped, retrieving the contents from the database 20 to generate a full-index 22 and a set of mini-indexes 24 which are utilized to generate and continually update the index information 26 in the database 28 to be consumed and served by the query node servers 30; and illustrated on the top right is an operation F that describes a second user who operates a client machine 33 to enter a query that is received by one or more query node servers 30 that, in turn, apply the query to the index information 26 to identify and return search results that reference the document 16. The above operations to continually rebuild the index information 26 are performed in real-time and without interruption to service that is provided to the first and second users who continue to interact with the system 10.

The index information 26 may include an inverted index 32 and document information 34. An inverted index (e.g., inverted index 32), as is well known in the art, is an index data structure storing a mapping from content (e.g., content contained by the document 16), such as words or numbers, to its locations in a database file, or in a document (e.g., document 16) or a set of documents. The documents 16 (e.g., document data, column group data) and/or information contained by the documents 16 may be stored in the document information 34.

Merely for example a “document X” may include the words “apple,” “orange,” and “banana;” a “document Y” may include the words “apple” and “orange; and, a “document Z” may include the word “apple.” An inverted index for the words in documents X, Y, and Z may be generated as follows:

Word   Document
apple  X(1), Y(1), Z(1)
orange X(2), Y(2)
banana X(3)

The above inverted index may be utilized to identify the word “apple” as being positioned in the first word of documents X, Y, and Z; the word “orange” as being positioned in the second word of the documents X and Y; and the word “banana” as being positioned as the third word of the document X. Accordingly, the above inverted index may be utilized to map a keyword “apple” contained in a query that is received from a client computer to the documents X, Y, and Z that are further referenced in search results that are returned to the client computer. It is appreciated by one skilled in the art that the inverted index 32 corresponds to the underlying database that it describes. Accordingly, any update to the underlying database is reflected in a corresponding update to the inverted index 32. Updates to the database 28 may include the addition and deletion of documents 16 in the document information 34 as well as the update of any of the contents contained by the documents 16 in the document information 34. In the present embodiment, the index information 26 may be updated in real time to respond to a query in real time with accurate search results that include the most recent document information 34. To this end, the operations A-F are now further described.

The information storage and retrieval platform 11 includes multiple components including the application servers 14 that may execute on one or more application server machines (not shown), the database 18, a database 20, an Hadoop distributed file system 23, the database 28, the query node servers 30 that operate on query node server machines (not shown), an Hbase/Hadoop Cluster 44 comprised of one or more Hbase/Hadoop machines (not shown) including an Hbase Hadoop Node 49 (e.g, Hbase/Hadoop machine), an index distribution module 52 executing on Hbase/Hadoop machine, search front end servers 58 that executes on search machines (not shown), and search back end servers 60 that execute on search machines (not shown) as being communicatively coupled together. For example, the multiple components may be communicatively coupled with any combination of a wide area network, local area network, wireless network, or any other type of network utilizing various networking technologies.

At operation A, the document 16, or one or more elements of the document 16, may be communicated from the client machine 12 to the application servers 14 and stored in the database 18 (e.g., Oracle database). The document 16 may include multiple elements including elements a, b, c, d, e, and f that may include strings of text, numeric information, scores, or other discrete quantum of information that are positioned in different sections or fields of the document (e.g., item information).

At operation B, at the application servers 14, event manager modules 36 may identify updates to the database 18, generate events that correspond to the respective updates, prioritize the events according to the quality of the data in the event and communicate the prioritized events into event queues 38 that are consumed by consumer modules 40 that service the respective event queues 38. According to an embodiment, the event manager modules 36 and the consumer modules 40 may utilize three event queues 38 to process and prioritize event types. For example, the update of the “element a” in the document 16 in the database 18 may be a price change to item information describing an item for sale that causes the generation of a corresponding event that is associated with a high priority that, in turn, is communicated into in a first event queue associated with high priority that, in turn, is received by a consumer module 40. Similarly, the update of the “element b” in document 16 in the database 18 may be a change to a title of the item that causes the generation of an event that is associated with a medium priority that, in turn, is communicated into a second event queue associated with the medium priority that, in turn, is received by a consumer module 40. Finally, the update of the “element c” in document 16 in the database 18 may be a change to a description of the item that causes the generation of an event that is communicated into a third event queue associated with a low priority that, in turn, is received by a consumer module 40. Accordingly, the three event queues 38 may be utilized to communicate events in high, medium, and low priorities to facilitate a preference for the update of high priority events (e.g., price) over medium priority events (e.g., title) over low priority events (e.g., description). In some embodiments the priority for the respective event types may be configured. Other embodiments may include fewer or more event queues 38.

At operation C, the consumer modules 40 may transform the data in the events and communicate the transformed data via an HBase application programming interface to an HBase master server 42 in an HBase/Hadoop cluster 44 that, in turn, stores the transformed data in one or more tables including an items table 21 in the database 20 (e.g., HBase). The transformed data may be stored according to regions that are managed by region server processes 46. According to an embodiment, the database 20 may be embodied as an open source non-relational, distributed database (e.g., HBase) that runs on a Hadoop Distributed Filesystem (HDFS) 23. HDFS 23 is an open source software framework that supports data-intensive distributed applications, known by those skilled in the art. The Hbase/Hadoop cluster 44 may further includes the HBase master server 42 that is utilized to manage the HBase/HDFS environment, a scheduler module 48, and an HBase/Hadoop node 49 that includes multiple region server processes 46 and a map-reduce job module 50. Each region server process 46 may further be associated with a column (not shown) that corresponds to a range of documents (e.g., or items corresponding to item information in the items table 21) and may be utilized to manage one or more regions (not shown) that respectively correspond to a range of the documents 16. For example, the documents 16 may be uniquely identified with document identifiers (e.g., item identifiers) that are numbered from 0 to X where each column and region are dedicated to respective overlapping predetermined ranges of documents (e.g., documents (0-100 and documents (0-50), as described further in this document. According to one embodiment, the number of region server processes 46 may be in the hundreds but scaling is not limited to any fixed number. HBase is a technology that provides a fault-tolerant way of storing large quantities of sparse data featuring compression, in-memory operation, and a space-efficient probabilistic data structure (e.g., Bloom filters) on a per-column basis as outlined in the original BigTable paper, as is known by those skilled in the art. A table in the database 20 (e.g., HBase) may serve as the input and output for one or more map-reduce jobs that are scheduled by the map-reduce job module 50. The map-reduce jobs may be embodied as a map jobs and reduce jobs that runs in HDFS. The tables in the database 20 may further be accessed through the Java Application Programming Interface (API) but also through representational state transfer (REST) architecture and other APIs.

At operation D, the scheduler module 48, executing in the HBase/Hadoop cluster 44, may schedule two index generating sub-operations that process in parallel to generate indexes that are subsequently distributed to the query node servers 30. The sub-operations may execute for the generating of a full-index 22 and the generating of the mini-indexes 24. The sub-operations may further execute for the distribution of the indexes to the query node servers 30. The full-index 22 may be a snapshot of the contents of items table 21 in the database 20 and the mini-indexes 24 may respectively correspond to a series of consecutive snapshots where each snapshot captures one or more updates to the items table 21 in the database 20 that occurred within an associated time period of time. The distribution of the full-indexes 22 and the mini-indexes 24 to the query node servers 30 may be over a network utilizing an index distribution module 52 which is based on Bit Torrent, a peer to peer file sharing protocol. In one embodiment, the scheduler module 48 may schedule the generation of the full-index 22 twice in a twenty-four hour period and the generation of mini-indexes 24 every five minutes. The scheduler module 48 may generate a full-index 22 that is associated with a start-time by scheduling a map-reduce job module 50. The map-reduce job module 50 may initiate a map step that divides the job into smaller sub-jobs (e.g., map tasks) and multiple reduce steps that consume the output from the sub jobs and aggregates results to generate the index information 26. Similarly, the scheduler module 48 may generate a mini-index 24 by scheduling a map-reduce job module 50 for execution on the Hbase/Hadoop Node 49 may include a map step but not, according to one embodiment, a reduce step. Accordingly, each mini-index 24 may be associated with events that arrive from the event queues 38 during a particular period of time and is associated with one or more full-indexes 22. Each index 22, 24 (e.g., full and mini) may include a bill of material (BOM) information which describes the content of the index 22, 24 including the index information 26. The full-index 22 may include full-index BOM information 54 and the mini-index 24 may include mini-index BOM information 56. The index information 26 may include the inverted index 32 and document information 34, as previously described.

At operation E, each of the query node servers 30 may receive the full-index 22 and the associated mini-indexes 24. The query node servers 30 may be comprised of a search grid that is arranged in columns of query node servers 30, as described later in this document. Each column of query node serves 30 and may be utilized to manage a range of the documents 16 (e.g., column), as previously mentioned. The index information 26 may be stored in memory of the query node servers 30 and in the database 28 connected to the query node servers 30. The index information 26 may be updated with the full-index 22 responsive to its arrival at the query node servers 30. Further, the index information 26 may be updated with the mini-index 24 responsive to its arrival at the query node servers 30. The index information 26 is generally updated in sequential order. For example, the index information 26 are generally updated at the query node server 30 in the order in which the full-index 22 and the mini-indexes 24 are generated. To this end, the full-index 22 may be associated with full-index BOM information 54 the mini-index 24 may be associated with mini-index BOM information 56 that are utilized by the query node server 30 to manage the update of the index information 26. In one embodiment a map-reduce job module 50 may include sub jobs that execute on the Hbase/Hadoop node 49 to generate inverted indices in the form of region sub-indices (not shown) for part of the region associated with the region server (HBase). The sub jobs may further merge or stitch the multiple region sub-indices together for the region.

At operation F, a second user who operates the client machine 33 may enter a query that may be communicated over a network (e.g., Internet) via front-end servers 58 and back-end servers 60 to be received by the query node servers 30 which may be divided into two layers. The two layers may include an aggregation layer and a query execution layer. The aggregation layer may include a query node server 30 that includes a query engine 62 (e.g., query module) that receives the query that, in turn, communicates the query to multiple query engines 62 that respectively execute in the execution layer in multiple query node servers 30 that correspond to the columns. The query engines 62 in the query execution layer may, in turn, respectively apply the same query, in parallel, against respective the index information 26 that were generated for a range of document identifiers (e.g., column) to identify search results (e.g., document 16) in parallel. Finally, the query engines 62, at each query node servers 30 in the query execution layer, may communicate their respective partial search results to the query engine 62 in the aggregation layer which aggregates the multiple sets of partial search results to form a search result for the entire index information 26 and to communicate the search result over the network to the second user.

FIG. 2 is a block diagram of a profile configuration system 200, according to various embodiments. The system 200 may be implemented as a hardware or as software executed by hardware (e.g., by one or more processors) comprises a generator module 205, a receiver module 210, a modification module 215, a verification module 220, a search engine module 225, and a transformer module 230.

In various embodiments, the generator module 205 may be configured to generate a profile that includes behavior information organized as a plurality of components. The behavior information may be used to rank search results generated from a search query. In various embodiments, the behavior information describes of a plurality of factors and each factor may influence the ranking of search results. When a description of a factor in the behavior information is changed, the ranking of search results may also change. In various embodiments, the behavior information is organized as a plurality of components. The plurality of components may comprise the profile, at least one sub-profile, a blender, and at least one key where each key is paired with a value. In various embodiments, the components of the profile may be ordered according to a hierarchy. The hierarchy may order the components according to various schemes, such as location of implementation, type, and the like. In various embodiments, the behavior information may be transferred from a component within the profile. Alternatively, the behavior information may be transferred from a component included in a separate profile. In various embodiments, a lower ordered component may inherit behavior information from a higher ordered component. Alternatively, a higher ordered component may not inherit behavior information from a lower ordered component. In an example embodiment, the hierarchy may order profiles as being higher than sub-profiles. This ordering indicates that information from a sub-profile may be replaced by information from a profile. This ordering also indicates that information from the sub-profile may not replace information in the profile. In another example embodiment, the hierarchy may rank a country location as being higher than a region location. This ordering indicates that information from a component being implemented in the United States may be used to replace information in a component being implemented in California. In various embodiments, access to the profile may be limited to authorized users. A user may be authorized based on a user credential, such as identification, password, and the like. As such, a non-authorized user may not transfer information to the profile. In various embodiments, once behavior information in a component is modified, the ranking of the search results may change.

In various embodiments, the profile may be generated based on a template. The template may include a set of rules and comprise a plurality of components. In various embodiments, the set of rules for the template may indicate a maximum number of components for the template. In various embodiments, the set of rules may order the components in the template according to the hierarchy. The ordering of each component in the template may determine whether or not behavior information is transferred from one component to another component. In various embodiments, the behavior information may comprise of various types. For instance, the behavior information may be classified as “category,” “site,” and the like. The type of behavior information may indicate the type of search results the behavior information may influence. In various embodiments, the set of rules may also indicate types of behavior information for which the template may inherit from. As an example, the set of rules may indicate that the template may inherit behavior information classified as “category.” In various embodiments, if a profile is generated based on the template, the profile adheres to the set of rules for the template.

In various embodiments, the receiver module 210 may be configured to receive additional behavior information from a user. In various embodiments, the behavior information received from the user may comprise of various types. For instance, the behavior information may be classified as “category,” “site,” and the like. In various embodiments, the additional behavior information may be organized as a component. Alternatively, the additional behavior information may be organized as a plurality of components. In various embodiments, the additional behavior information received from the user may be a second profile organized as a plurality of components.

In various embodiments, the modification module 215 may be configured to update at least one component of the profile using the additional behavior information received from the user. As mentioned previously, the additional behavior information may be organized as a component or as a plurality of components. In various embodiments, the additional behavior information may replace the behavior information from one component in the profile. Alternatively, the additional behavior information may replace the behavior information from two or more components in the profile. In various embodiments, the modification module 215 may add the additional behavior information to the profile. The additional behavior information may be added as a component or as a plurality of components to the profile. Alternatively, the additional behavior information may be added to the behavior information from one or more components in the profile.

In various embodiments, the verification module 220 may be configured to determine that the profile may be updated using the additional behavior information. As mentioned previously, the components of the profile may be ordered according to a hierarchy. The component of the additional behavior information may also be ordered according to the same hierarchy. The rank of a component in the hierarchy may indicate an order of inheritance for the component. The order of inheritance may determine whether or not behavior information is transferred from one component to another component. In various embodiments, the verification module 220 may determine whether the component of the additional behavior information has a higher order than a component from the profile. The verification module 220 may determine that the component of the additional behavior information has a higher order than the component from the profile and therefore the component from the profile may be updated using the additional behavior information. Alternatively, the verification module 220 may determine that the component of the additional behavior information has a lower order than the component from the profile and therefore the profile may not be updated using the additional behavior information. As an example, the receiver module 210 may receive additional behavior information organized as a sub-profile from the user. The verification module may determine that the sub-profile is ordered higher than a component from the profile and therefore allow the additional behavior information to replace the information in the component from the profile.

In various embodiments, information may be transferred between components of the same type. As such, the verification module 220 may determine that the component of the additional behavior information is of the same type as the component from the profile and therefore the profile may be updated using the additional behavior information. In various embodiments, the verification module 220 is further configured to determine that the user is authorized to update the profile using the additional behavior information. The determination may be based on the user's credentials, such as an identification of the user, an account password, and the like. In various embodiments, the modification module 215 may update the profile using the additional behavior information after receiving verification from the verification module 220.

In various embodiments, the verification module 220 may determine that the profile is generated based on a template. The verification module 220 may use the set of rules for the template to determine that the profile may be updated using the additional behavior information. In various embodiments, the verification module 220 may determine that the additional behavior information is of a type allowed by the set of rules for the template and therefore allow the profile to be updated. In various embodiments, the verification module 220 may determine that if the additional behavior information is used to update the profile, the updated profile will not violate the maximum number of components as indicated by the set of rules for the template. In various embodiments, the verification module 220 may determine that if the additional behavior information is used to update the profile, it does not violate the maximum number of times the template may inherit behavior information, as indicated by the set of rules for the template.

In various embodiments, the search engine module 225 may be configured to receive a search query and generate search results based on the search query. In various embodiments, the search query may be received from a user at the query node server 24 to search a database 26 as depicted in FIG. 1. The search engine module 225 may be further configured to rank the search results based on the profile updated by the modification module 215. In various embodiments, the ranking of the search results may be based on the behavior information included in the updated profile. As mentioned previously, the behavior information describes a plurality of factors each of which may influence the ranking of search results. In various embodiments, the ranking of search results using the updated profile may be different than the ranking of search results using the profile prior to modification. In various embodiments, there may be a plurality of generated profiles organized as a plurality of components. The plurality of components within each generated profile may be ordered according to a single hierarchy or may be ordered according to a plurality of hierarchies. In other words, the plurality of components in a first profile may be ordered according to a first hierarchy. And the plurality of components in a second profile may be ordered according to a second hierarchy. Moreover, there may be a plurality of templates each with a set of rules. In various embodiments, the set of rules from one template may be different from the set of rules from another template. In various embodiments, a plurality of profiles may be used by the search engine module 225 to influence the ranking of search results.

In various embodiments, the profile may be represented in a simplified form. A simplified profile may include behavior information expressed in terms of variables. The variables may be a short form representation of a longer expression. In various embodiments, expressing the behavior information in terms of variables allows the user send the additional behavior information to the system 200 more easily. In various embodiments, the transformer module 230 may be configured to transform the variables in the profile to its longer expression. After transformation, the profile may be a fully expanded profile. The transformed profile may then be used to communicate with the system 200. In various embodiments, the simplified profile may not be implemented by the system 200 until it is transformed by the transformer module 230.

FIG. 3 is a flowchart of a method 300 to update a profile and rank search results using the updated profile, according to various embodiments. The method 300 beings at step 302 when the generator module 205 generates a profile that includes behavior information organized as a plurality of components. At step 304 the receiver module 210 may receive additional behavior information from a user. At step 306, the modification module 215 may update at least one component of the plurality of components using the additional behavior information received from the user. At step 308, the search engine module 225 may receive a search query and may generate search results based on the search query. At step 310, the search engine module 225 may rank the search results based on the updated profile.

FIG. 4 is a block diagram of a profile 402 and additional behavior information being used to update the profile 402, according to an embodiment. In various embodiments, the profile 402 may include a profile component 404, a sub-profile component A 406 that includes a key value component 408, and a blender component 410. Moreover, both the sub-profile component A 406 and the blender component 410 may be included within the profile component 402 of the profile component 404. In various embodiments, additional behavior organized as a sub-profile component B 412 may be used to update the sub-profile component A 406 of the profile 402. In various embodiments, each component of the profile 402 may include a component order indicating an order of an inheritance. As mentioned previously, a lower ordered component may inherit behavior information from a higher ordered component. Further, the verification module 220 may determine that the sub-profile component A 406 and sub-profile component B 412 are components of the same type. As mentioned previously, information may be transferred between components of the same type. Therefore the verification module 220 may allow the additional behavior information organized as the sub-profile component B 412 to replace the sub-profile component A 406.

FIG. 5 is a block diagram of a profile 502, according to an embodiment, that is updated. The profile 502 that is updated may include a profile component 504 that further includes a sub-profile component B 506 and a blender component 508. In various embodiments, the profile 502 that is updated result from updating the profile 402, as shown on FIG. 4, with the additional behavior information organized as the sub-profile component B 412, also shown on FIG. 4. Accordingly, search results that are ranked using the profile 502 may be different from search results that are ranked using the profile 402.

FIG. 6 is a diagram 600 depicting a component of a profile, according to various embodiments. In various embodiments, the component of the profile may comprise ranking expressions used to influence the ranking of search results.

FIG. 7 is a diagram 700 depicting a component of a profile, according to various embodiments. In various embodiments, the component of the profile may comprise ranking expressions used to influence the ranking of search results.

FIG. 8 is a diagram 800 depicting a component of a profile, according to various embodiments. In various embodiments, a component of a profile may be a sub-profile component that comprises behavior information used to influence the ranking of search results.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied (1) on a non-transitory machine-readable medium or (2) in a transmission signal) or hardware-implemented modules. A hardware-implemented module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more processors may be configured by software (e.g., an application or application portion) as a hardware-implemented module that operates to perform certain operations as described herein.

In various embodiments, a hardware-implemented module may be implemented mechanically or electronically. For example, a hardware-implemented module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware-implemented module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware-implemented module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily or transitorily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering embodiments in which hardware-implemented modules are temporarily configured (e.g., programmed), each of the hardware-implemented modules need not be configured or instantiated at any one instance in time. For example, where the hardware-implemented modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware-implemented modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware-implemented module at one instance of time and to constitute a different hardware-implemented module at a different instance of time.

Hardware-implemented modules can provide information to, and receive information from, other hardware-implemented modules. Accordingly, the described hardware-implemented modules may be regarded as being communicatively coupled. Where multiple of such hardware-implemented modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware-implemented modules. In embodiments in which multiple hardware-implemented modules are configured or instantiated at different times, communications between such hardware-implemented modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware-implemented modules have access. For example, one hardware-implemented module may perform an operation, and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware-implemented module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware-implemented modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information).

The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules.

Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or processors or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations.

The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs).)

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Example embodiments may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.

A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

In example embodiments, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method operations can also be performed by, and apparatus of example embodiments may be implemented as, special purpose logic circuitry, e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC).

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In embodiments deploying a programmable computing system, it will be appreciated that that both hardware and software architectures require consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware may be a design choice. Below are set out hardware (e.g., machine) and software architectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 9 is a block diagram of machine in the example form of a computer system 900 within which instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

The example computer system 900 includes a processor 902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 904 and a static memory 906, which communicate with each other via a bus 908. The computer system 900 may further include a video display unit 910 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 900 also includes an alphanumeric input device 912 (e.g., a keyboard or a touch-sensitive display screen), a user interface (UI) navigation device 914 (e.g., a mouse), a disk drive unit 916, a signal generation device 918 (e.g., a speaker) and a network interface device 920.

Machine-Readable Medium

The disk drive unit 916 includes a machine-readable medium 922 on which is stored one or more sets of instructions and data structures (e.g., software) 924 embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 924 may also reside, completely or at least partially, within the main memory 904 and/or within the processor 902 during execution thereof by the computer system 900, the main memory 904 and the processor 902 also constituting machine-readable media.

While the machine-readable medium 922 is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions or data structures. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 924 may further be transmitted or received over a communications network 926 using a transmission medium. The instructions 924 may be transmitted using the network interface device 920 and any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.

Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.

Claims

1. A system comprising:

a generator module configured to generate a profile that includes behavior information organized as a plurality of components;

a receiver module configured to receive additional behavior information from a user;

a modification module configured to update at least one component of the plurality of components using the additional behavior information received from the user; and

a search engine module configured to receive a search query, generate search results based on the search query, and rank the search results based on the updated profile.

2. The system of claim 1, further comprising:

a verification module configured to determine that the profile may be updated using the additional behavior information.

3. The system of claim 2, wherein the verification module is further configured to determine that the user is authorized to update the profile using the additional behavior information.

4. The system of claim 1, wherein the modification module is configured to replace at last one component of the behavior information in the profile with the additional behavior information.

5. The system of claim 1, wherein the modification module is configured to add the additional behavior information to at least component in the profile.

6. The system of claim 1, wherein the plurality of components are ordered according to a hierarchy.

7. The system of claim 1, wherein the behavior information includes a plurality of keys and each name corresponds to a value.

8. The system of claim 1, wherein the profile is generated based on a template.

9. The system of claim 1, further comprising:

a transformer module configured to transform the profile into a fully expanded profile.

10. A method comprising:

generating a profile that includes behavior information organized as a plurality of components;

receiving additional behavior information from a user;

updating at least one component of the plurality of components using the additional behavior information received from the user;

receiving a search query and generating search results based on the search query; and

ranking the search results based on the updated profile.

11. The method of claim 10, further comprising:

determining that the profile may be updated using the additional behavior information.

12. The method of claim 11, further comprising:

determining that the user is authorized to update the profile using the additional behavior information.

13. The method of claim 10, wherein updating at least one component of the plurality of components includes replacing at last one component of the behavior information in the profile with the additional behavior information.

14. The method of claim 10, wherein updating at least one component of the plurality of components includes adding the additional behavior information to at least one component in the profile.

15. The method of claim 10, wherein the plurality of components are ordered according to a hierarchy.

16. The method of claim 10, wherein the behavior information includes a plurality of keys and each name corresponds to a value.

17. The method of claim 10, wherein the profile is generated based on a template.

18. The method of claim 10, further comprising:

transforming the profile into a fully expanded profile.

19. A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

generating a profile that includes behavior information organized as a plurality of components;

receiving additional behavior information from a user;

updating at least one component of the plurality of components using the additional behavior information received from the user;

receiving a search query and generating search results based on the search query; and

ranking the search results based on the updated profile.