Abstract: A method for adaptive code processing based on artificial intelligence, including: obtaining a candidate code set, wherein the candidate code set comprises a plurality of candidate codes, first description information corresponding to each candidate code, and second description information corresponding to each code slice in the candidate codes; determining a similarity between every two candidate codes in the candidate code set based on a plurality pieces of first description information and a plurality pieces of second description information; obtaining a new candidate code set by performing crossover and mutation on two candidate codes with a similarity less than a first threshold; and returning to the step of determining the similarity based on the new candidate code set until a target code set meeting a requirement is obtained.

Abstract: A method for generating an adaptive program based on artificial intelligence (AI) performed by an agent in an electronic device. The method includes: determining diversity information of a target population, wherein the diversity information indicates program diversity of the target population; determining a temperature parameter according to the diversity information, wherein the temperature parameter is used to adjust a selection pressure; selecting, from the target population according to the temperature parameter, a first parent program and a second parent program corresponding to the first parent program; and obtaining a target program by performing an evolution iteration according to the first parent program and the second parent program.

Abstract: A communication method, apparatus, electronic device and storage medium for a computing power cluster are provided. An implementation of the method includes: during a process of communicating with a communication receiver using Remote Direct Memory Access (RDMA) protocol, obtaining a first packet loss rate corresponding to the RDMA protocol; in response to the first packet loss rate being higher than a first preset packet loss rate, initiating a first handshake request to the communication receiver for requesting to switch to Transmission Control Protocol (TCP) for communication; receiving a first handshake response returned by the communication receiver for the first handshake request, and determining a first starting transmission position of data according to a last data receiving position in the first handshake response; and communicating, by using the TCP, with the communication receiver starting from data corresponding to the first starting transmission position.

Abstract: A method and apparatus for cross-computing power cluster communication, an electronic device, and a computer readable storage medium, are provided. An implementation of the method includes: in response to a communication initiator and a communication receiver respectively belonging to different computing power clusters, increasing a RDMA connection count on the basis of an initial connection count until a detected actual bandwidth value no longer increases, to obtain a target connection count; increasing, with the RDMA connection count being maintained at the target connection count, a Buffer size on the basis of an initial size until a detected actual bandwidth value no longer increases, to obtain a target size; determining cross-cluster transmission parameters, based on the target connection count and the target size; and communicating, cross-cluster transmission parameters, with the communication receiver belonging to a different computing power cluster.

Abstract: A communication method, apparatus, electronic device and storage medium for a computing power cluster are provided. An implementation of the method includes: during a process of communicating with a communication receiver using Remote Direct Memory Access (RDMA) protocol, obtaining a first packet loss rate corresponding to the RDMA protocol; in response to the first packet loss rate being higher than a first preset packet loss rate, initiating a first handshake request to the communication receiver for requesting to switch to Transmission Control Protocol (TCP) for communication; receiving a first handshake response returned by the communication receiver for the first handshake request, and determining a first starting transmission position of data according to a last data receiving position in the first handshake response; and communicating, by using the TCP, with the communication receiver starting from data corresponding to the first starting transmission position.

Abstract: A method and apparatus for cross-computing power cluster communication, an electronic device, and a computer readable storage medium, are provided. An implementation of the method includes: in response to a communication initiator and a communication receiver respectively belonging to different computing power clusters, increasing a RDMA connection count on the basis of an initial connection count until a detected actual bandwidth value no longer increases, to obtain a target connection count; increasing, with the RDMA connection count being maintained at the target connection count, a Buffer size on the basis of an initial size until a detected actual bandwidth value no longer increases, to obtain a target size; determining cross-cluster transmission parameters, based on the target connection count and the target size; and communicating, cross-cluster transmission parameters, with the communication receiver belonging to a different computing power cluster.

Abstract: In this invention, systems and methods for providing keywords for advertising are provided. After a user searches for another user in a social network, the webpage or blog of the queried user is retrieved, and keywords are extracted from this webpage. The keywords may be extracted from the user's profile on the social network (e.g., favorite sports, music artists, etc.), or keywords may be extracted from the text of the webpage (e.g., comments that comprise the blog entries). Once extracted, these keywords may then be used by an advertising system to provide targeted advertisements to the user.

Abstract: A system, media, and method for selecting future queries are provided. The selected future queries are used to transmit appropriate online advertising to a user that issues queries to a search engine. The search engine is coupled to a prediction component that predicts what subject the user is going to be interested in and when the user will be interested in the subject. The prediction component returns a future query using statistical language models representing a query history of the user and aggregate query histories for a community of users.

Abstract: This application describes a system and method for estimating user intent towards categories of content. The estimation of user intent may be based at least in part on a score for prior user actions and a decay function that is applied to that score to provide an estimate of current user intent. The estimate represents current user intent for time periods in which user actions towards a category of content are negligible or non-existent.

Abstract: Methods and computer-storage media having computer-executable instructions embodied thereon that facilitate classifying user intent with respect to an entity using intent-strength scores. A user query indicating a particular entity is received. The user's intent with respect to the particular entity is determined by assigning an intent-strength score to the user. The user's intent-strength score is determined using intent-strength scores assigned to seed patterns identified for entities in a category, as well as the received user query. In embodiments, a user's intent-strength score may be updated based on a subsequent query, or may be changed according to a function. A list of users having particular intent-strength scores for particular entities may be also be generated.

Abstract: A system, media, and method for selecting future queries are provided. The selected future queries are used to transmit appropriate online advertising to a user that issues queries to a search engine. The search engine is coupled to a prediction component that predicts what subject the user is going to be interested in and when the user will be interested in the subject. The prediction component returns a future query using statistical language models representing a query history of the user and aggregate query histories for a community of users.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate advertisement selection using advertising units. An entity that is a sequence of two or more words is referenced. The entity includes substrings comprising a portion of the entity. Search data in association with the entity is compared to corresponding search data in association with the substrings of the entity. Based on the comparison, it is determined that the entity comprises an advertisable unit that functions as a unit for purposes of selecting an advertisement for display. The advertisable unit is used to select an advertisement to be presented to the user.

Abstract: A system, media, and method for selecting future queries are provided. The selected future queries are used to transmit appropriate online advertising to a user that issues queries to a search engine. The search engine is coupled to a prediction component that predicts what subject the user is going to be interested in and when the user will be interested in the subject. The prediction component returns a future query using statistical language models representing a query history of the user and aggregate query histories for a community of users.

Abstract: Methods and computer-readable media are provided for defining user intent so that user intent can be determined and advertisements and other information can be provided to a user based on that user's intent. A topical expression is identified and is associated with attributes and actions. Actions indicate steps that can be performed to achieve a task associated with the topical expression. An intent structure is then generated. The intent structure illustrates a relationship between the identified topical expression and other topical expressions. User data may then be received and mapped to the intent structures to determine present and future user intent.

Abstract: This patent application pertains to selecting advertisements for presentation with search results. One example includes receiving a user's search query. This example also includes selecting an advertisement that is relevant for presentation to the user with search results for the search query. The advertisement is selected from a set of advertisements by comparing a semantic context of individual advertisements to a semantic context of the user's search query.

Abstract: Query classification techniques attempt to classify user search queries in order to better understand user search intent. Understanding a user's search intent allows search engines to provide relevant content tailored to the user's interest. Unfortunately, current classification techniques do not take into account contextual information. Accordingly, as provided herein, a target query may be classified based upon contextual information. In particular, features may be extracted from contextual information and/or other sources. For example, features may be extracted from the target query, related queries, and/or invoked search results of the related queries. In this way, the target query may be classified based upon other queries performed by the user and/or search results of the queries the user found interesting. In addition, a CRF model may be utilized in classifying the target query by providing generalized parameters learned from labeled query sessions.

Abstract: A system, media, and method for selecting future queries are provided. The selected future queries are used to transmit appropriate online advertising to a user that issues queries to a search engine. The search engine is coupled to a prediction component that predicts what subject the user is going to be interested in and when the user will be interested in the subject. The prediction component returns a future query using statistical language models representing a query history of the user and aggregate query histories for a community of users.

Abstract: A system, media, and method for selecting future queries are provided. The selected future queries are used to transmit appropriate online advertising to a user that issues queries to a search engine. The search engine is coupled to a prediction component that predicts what subject the user is going to be interested in and when the user will be interested in the subject. The prediction component returns a future query using statistical language models representing a query history of the user and aggregate query histories for a community of users.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate advertisement selection using advertising units. An entity that is a sequence of two or more words is referenced. The entity includes substrings comprising a portion of the entity. Search data in association with the entity is compared to corresponding search data in association with the substrings of the entity. Based on the comparison, it is determined that the entity comprises an advertisable unit that functions as a unit for purposes of selecting an advertisement for display. The advertisable unit is used to select an advertisement to be presented to the user.

Abstract: The claimed subject matter is directed to constructing query hierarchies in response to a query request. To construct a query hierarchy, a list of related candidate queries is generated in response to the received query request. The list of related candidate queries is generated by determining the relative coverage of information shared by the candidate queries and the query request. Relationships between the submitted query request and the candidate queries in the list are determined based upon the extent of relative coverage of information shared by the candidate queries and the query request. A query hierarchy is then constructed to reflect the determined relationships between the query request and the candidate queries.