Abstract: A method of model training includes obtaining a data set for training an interest alignment model of cross domain recommendation, the data set includes first domain data and second domain data. The method also includes performing a first feature extraction on the first domain data according to the interest alignment model to obtain a first domain feature representation and performing a second feature extraction on the second domain data according to the interest alignment model to obtain a second domain feature representation. Further, the method includes performing an interest alignment between the first domain and the second domain according to the interest alignment model based on the first domain feature representation and the second domain feature representation; and training the interest alignment model in a direction that reduces at least one of a first loss and a second loss, to obtain a trained interest alignment model.

Abstract: A content recommendation method is provided. A first feature representation of a first user and a second feature representation of a second user are obtained. A cluster center corresponding to the first user is determined. An extra-domain feature representation and intra-domain feature representation of the first user are obtained. The intra-domain feature representation is based on mapping the extra-domain feature representation with the mapping relationship function. A target feature representation of the first user is determined based on the intra-domain feature representation and the first feature representation. Target content that matches the target feature representation of the first user is determined. The target content is pushed to the first user.

Abstract: A resource-push method is disclosed. The method includes obtaining, by a resource-push server, a target relationship chain of a benchmark user; determining at least one relational user of the benchmark user according to the benchmark user and the target relationship chain; and obtaining parameter characteristics of the at least one relational user from a preset database. The method also includes determining a similarity value between each of the at least one relational user and the benchmark user according to the parameter characteristics, or a probability value of each of the at least one relational user according to the parameter characteristics; and determining at least one push user from the at least one relational user according to the similarity value or the probability value, so as to push a target resource to the at least one push user.

Abstract: A natural person information setting method, an account information providing method, and electronic devices include: an associated set of each account is determined according to a degree of association and an exclusion index of another account with the each account; convergence processing is performed on all associated sets, such that after the convergence processing, no associated account in any one associated set appears in another associated set; and identical natural person information is set for any associated account in a same associated set having gone through the convergence processing, and a same natural person is set for any associated account in the same associated set according to the identical natural person information.

Abstract: A method for pushing recommendation information is provided, and the method includes: obtaining a meta path that connects a candidate user and a target user in a heterogeneous information network, the meta path comprising a connection between the candidate user and a candidate recommendation-object and having an attribute value; obtaining a user similarity between the target user and the candidate user relative to the meta path; estimating an attribute value of a connection between the candidate recommendation-object and the target user according to the attribute value of the connection between the candidate user and the candidate recommendation-object, an attribute value constraint condition of the meta path, and the user similarity; and sending recommendation information of the candidate recommendation-object to a terminal corresponding to the target user when the estimated attribute value meets a recommendation condition.

Abstract: A resource-push method is disclosed. The method includes obtaining, by a resource-push server, a target relationship chain of a benchmark user; determining at least one relational user of the benchmark user according to the benchmark user and the target relationship chain; and obtaining parameter characteristics of the at least one relational user from a preset database. The method also includes determining a similarity value between each of the at least one relational user and the benchmark user according to the parameter characteristics, or a probability value of each of the at least one relational user according to the parameter characteristics; and determining at least one push user from the at least one relational user according to the similarity value or the probability value, so as to push a target resource to the at least one push user.

Abstract: A natural person information setting method, an account information providing method, and electronic devices include: an associated set of each account is determined according to a degree of association and an exclusion index of another account with the each account; convergence processing is performed on all associated sets, such that after the convergence processing, no associated account in any one associated set appears in another associated set; and identical natural person information is set for any associated account in a same associated set having gone through the convergence processing, and a same natural person is set for any associated account in the same associated set according to the identical natural person information.

Abstract: Disclosed are a natural person information setting method, an account information providing method, and electronic devices. The natural person information setting method includes the following steps: an associated set of each account is determined according to a degree of association and an exclusion index of another account with the each account; convergence processing is performed on all associated sets, such that after the convergence processing, no associated account in any one associated set appears in another associated set; and identical natural person information is set for any associated account in a same associated set having gone through the convergence processing, and a same natural person is set for any associated account in the same associated set according to the identical natural person information. Also disclosed are an account information providing method and electronic devices.

Abstract: Described are a method and an apparatus for classifying an object based on a social networking service. Feature information of an object may be acquired; a semantic vector of the feature information may be computed, the semantic vector being used to reflect a relevancy feature when segmented words exist in the feature information simultaneously; and the semantic vector of the feature information of the object may be input to a predetermined classifier, to obtain an initial category of the object after the object is classified.

Abstract: A method for pushing recommendation information is provided, and the method includes: obtaining a meta path that connects a candidate user and a target user in a heterogeneous information network, the meta path comprising a connection between the candidate user and a candidate recommendation-object and having an attribute value; obtaining a user similarity between the target user and the candidate user relative to the meta path; estimating an attribute value of a connection between the candidate recommendation-object and the target user according to the attribute value of the connection between the candidate user and the candidate recommendation-object, an attribute value constraint condition of the meta path, and the user similarity; and sending recommendation information of the candidate recommendation-object to a terminal corresponding to the target user when the estimated attribute value meets a recommendation condition.

Abstract: The present disclosure provides a group positioning method; the method includes: acquiring position information corresponding to positioned user identifiers in a group, to obtain a position information set; and determining a group central position according to the position information set, the number of geographical distance values that are less than or equal to a preset distance threshold among geographical distance values between the group central position and geographical positions represented by position information in the position information set being maximized. With the group positioning method provided by the present disclosure, the number of group members carrying out activities in a circular area that uses the group central position as the center and the preset distance threshold as the radius is maximized.

Abstract: Described are a method and an apparatus for classifying an object based on a social networking service. Feature information of an object may be acquired; a semantic vector of the feature information may be computed, the semantic vector being used to reflect a relevancy feature when segmented words exist in the feature information simultaneously; and the semantic vector of the feature information of the object may be input to a predetermined classifier, to obtain an initial category of the object after the object is classified.

Abstract: The present disclosure provides a group positioning method; the method includes: acquiring position information corresponding to positioned user identifiers in a group, to obtain a position information set; and determining a group central position according to the position information set, the number of geographical distance values that are less than or equal to a preset distance threshold among geographical distance values between the group central position and geographical positions represented by position information in the position information set being maximized. With the group positioning method provided by the present disclosure, the number of group members carrying out activities in a circular area that uses the group central position as the center and the preset distance threshold as the radius is maximized.

Abstract: Disclosed are a natural person information setting method, an account information providing method, and electronic devices. The natural person information setting method includes the following steps: an associated set of each account is determined according to a degree of association and an exclusion index of another account with the each account; convergence processing is performed on all associated sets, such that after the convergence processing, no associated account in any one associated set appears in another associated set; and identical natural person information is set for any associated account in a same associated set having gone through the convergence processing, and a same natural person is set for any associated account in the same associated set according to the identical natural person information. Also disclosed are an account information providing method and electronic devices.