Abstract: Ranking search results based on color including evaluating whether a color term in a search query was intended to identify an actual color and returning different search results based on that determination. When an actual color was intended, an initial product ranking is updated based on color similarity of the product. In some embodiments, a color similarity score is calculated using a color difference formula, a cosine similarity measurement is determined of a cosine of an angle between a query vector and a title vector, and the color is considered to have been intended to identify the actual color when the cosine similarity measurement is less than a threshold value. In particular embodiments, the query vector includes individual terms of the search query and a number of occurrences of each individual term. Different embodiments include computer implemented systems and methods.

Abstract: A system for displaying product information is described herein. The system includes a database and a server including a processor that is programmed to receive search data associated with a product search request received and generate a ranked product list in response to the product search request. The processor selects a first sorting value indicative of a first data category included in the search data, generates a first sorted group of product records as a function of the first sorting value, and stores the first sorted group in the database. The processor selects a second sorting value indicative of a second data category, generates a second sorted group of product records as a function of the first sorted group and the second sorting value, and generates the ranked product list as a function of the second sorted group.

Abstract: A system for monitoring consumer activity on a website is described herein. The system includes a memory device and an activity sessionization server. The memory device is configured to store a webpage list including a plurality of webpage records and an activity session list including a plurality of activity session records. Each of the activity session records including a webpage URL and a session ID. The activity sessionization server retrieves a webpage record from the webpage list, determines if the retrieved webpage record includes a referral URL, identifies a matching activity session record that includes a corresponding webpage URL that matches the referral URL of the retrieved webpage record, determines a corresponding session ID associated with the matching activity session record, and associates the corresponding session ID with the current activity session record.

Abstract: Example systems and methods that select search result images are described. In one implementation, a method accesses a ranking of multiple products associated with a search query and identifies a reference to a color in the search query. The method identifies a first product from the ranking of multiple products and identifies multiple product images associated with the first product. A color determination is made regarding each of the multiple product images. A product image having an associated color that is most similar to the color in the search query is selected for presentation in the search results.

Abstract: Queries are analyzed to identify start-side sub-queries that include a starting word of the query and one or more contiguous words and end-side sub-queries that include an ending word of the query and one or more contiguous words. A start-side count is determined for a sub-query indicating a number of times it occurs among the queries as a start-side query. An end-side count is also computed for each sub-query indicating the number of times it occurs as an end-side sub-query. A score is computed for each sub-query that decreases with the start-side count and increases with the end-side count. A search is performed for a received query with some of the phrases (e.g. noun phrases) thereof weighted according to the scores. Noun phrases may be identified as the longest n-gram for which an entry in a reference dictionary exists.

Abstract: Systems and methods are disclosed herein for providing search results, particularly product records from a product database. Past queries are analyzed and grouped into clusters according to similarities, with each query including a highly relevant head query and a plurality of tail queries. Similarity of queries may be determined based on user response similarity to query results, co-occurrence, and textual similarity. One or more categories are identified for the clusters, such as based on click-through rates for search results of the head queries of the clusters. Upon receiving a query, a cluster for the query is identified, such as according to similarity to one or more queries of the cluster. The categories associated with the cluster are then used to one or both of augment the query and rank search results for the query.

Abstract: Providing information to a module is disclosed. A first query and one or more subject type concepts associated with the first query is received. A second query to be provided to the module based at least in part on the received first query and subject type concepts is determined. The second query is sent to the module.

Abstract: A method of computing a featured set of product groups for a query on an inventory of products. Each of the products can have one or more attribute-value pairs. Each of the one or more attribute-value pairs can have an attribute. The method can include determining a result group of the products matching the query. The method also can include determining relevance scores for the product groups. The method further can include determining a featured attribute and the featured set of the product groups for the featured attribute. The method also can include, after receiving the query from a user, transmitting for display at least one page of a website, where the at least one page has an option to view each of the product groups of the featured set of the product groups that has been selected. Other embodiments of related systems and methods are also disclosed.

Abstract: Systems and methods are disclosed herein for providing search results, particularly product records from a product database. Past queries are analyzed and grouped into clusters according to similarities with one another. For an individual query, relevant search results are identified and departments corresponding thereto are also identified. Confidence scores are determined for the identified departments and if one or more departments have confidence scores above a threshold, the search results are filtered based thereon. If not, then departments corresponding to a query cluster relevant to the query are selected. Additional departments may also be selected based on the number of search results corresponding to the additional departments. Search results filtered according to one or more selected departments are transmitted to an author of the query.

Abstract: Computer-implemented methods and systems may be used for identifying an attribute and/or attribute value in a text string. In embodiments, the text string comprises a search query submitted by a user. Embodiments of the present disclosure include identifying an attribute value from a search query by comparing the search query string to a list of known attribute values and comparing the candidate attribute value to a knowledge base to confirm that the string represents an attribute value rather than a non-attribute concept. In embodiments, a Bloom filter is employed to execute a relatively efficient comparison between a candidate attribute value and known non-attribute concepts.

Abstract: Systems and methods are disclosed herein for providing search results, particularly product records from a product database. Products records have facet groups associated therewith and facet groups have probabilities associated therewith indicating the usefulness of the facet group as a way of organizing or filtering search results. For a query, relevant product records are identified. For facet groups present among the relevant product records entropy values are calculated based on a distribution of values for the attribute defining the facet group. The entropy value increases as the distribution of values becomes more uniform. The facet group probability and entropy value are combined to assign a score to the facet groups. The facet groups are ranked and selected according to the score and search results are presented with indicators of the facet groups to enable a user to manage the search results.

Abstract: Selectively delivering an article is disclosed. A user preference is received. A document is also received. An entity pair comprising a concept included in a concept taxonomy and a textual representation included in the document is determined. The document is categorized based at least in part on the concept. A user associated with the user preference is selectively notified of the document.

Abstract: Delivering a page that includes a plurality of modules is disclosed. A query is received. Subject type concepts associated with the query are determined. Candidate modules that correspond to the associated concepts are found. A plurality of modules is selected from the candidate modules to be delivered on a page.

Abstract: The present invention extends to methods, systems, and computer program products for improving search relevance. In one implementation, a method identifies a first group of mappings generated for dominant queries associated with a query log. The method also identifies a second group of mappings that includes mappings between a first set of queries and categories shown for the first set of queries, and mappings between a second set of queries and clicked products for the second set of queries. Upon receipt of a query, the method applies the second group of mappings for the received query if the category mappings for the received query are in the second group of mappings. Alternatively, the method applies the first group of mappings for the received query if the category mappings for the received query are in the first group of mappings but not in the second group of mappings.

Abstract: The present invention extends to methods, systems, and computer program products for training a classification model to predict categories. In one implementation, a method identifies category mappings generated for dominant queries associated with a query log. The method identifies mappings between a first set of queries and categories shown for the first set of queries, and identifies mappings between a second set of queries and clicked products for the second set of queries. A classification model is trained based on the mappings generated for dominant queries, the mappings between queries and the shown categories, and the mappings between queries and the clicked products.

Abstract: The present invention extends to methods, systems, and computer program products for classifying e-commerce queries to generate category mappings for dominant products. A query log is mined for any query records with click through information that indicates one or more products were selected from among corresponding query results. For each of one or more categories, a selection rate is calculated for any product selected from among at least one corresponding query result returned in response to a query of the category. A specified top number of products are identified in the category. The specified top number of products has higher selection rates relative to other products in the category. A category score is calculated for the category based on product information associated with the specified top number of products in the category. The one or more categories are ranked based on the calculated category scores.

Abstract: Example systems and methods that classify queries to generate category mappings are described. In one implementation, a method mines a query log to identify query records with click through information and display information that indicate one or more products were selected or displayed within a specified date range. A category is associated with each of the products. The method calculates a display rate and a selection rate for any product selected or displayed among the products. A mapping is identified between queries and the shown categories, and between queries and the clicked products. A category score is calculated for a particular category based on a number of times the category is shown and a number of times the category is clicked.

Abstract: Techniques for providing improved search results for queries are provided. The techniques include a server receiving a query from a customer and identifying a number of product listings corresponding to the query according to relevance scores. The techniques also include identifying a signal associated with each of the product listings, calculating a signal score from a relevance score and a signal value for each of the product listings, creating query results comprising the number of product listings ordered according to the signal score, and transmitting the query results to the customer.

Abstract: Techniques for providing improved search results for queries are provided. The techniques include obtaining a candidate pair from a query log comprising a single word unigram and a two word bigram which expresses the same content as the unigram, receive customer interaction data from the query log for the unigram and the bigram, and determining if the candidate pair includes a dominant form, equivalent form, or un-comparable pair. The techniques also include creating a rule regarding treatment of the unigram or bigram in analyzing a query. The techniques further include receiving a query, analyzing the query to identify modified query results according to the rule, and presenting the query results.

Abstract: The present disclosure is directed to positioning a new item in search results that may additionally include previously-released items. Items in a search result may be ranked by popularity scores. In embodiments of the present disclosure, a new item may receive a popularity score boost to compensate for that item's relative lack of history in comparison to the previously-released items. Embodiments of the present disclosure utilize outside data sources to rank the items. Outside data sources may include social media, knowledge bases, and like platforms to leverage available outside data to complement internal data.