A method and an apparatus for establishing and using a user recommendation model in a social network. The method includes obtaining training data from the social network, performing heterogeneous data transfer learning on the training data to learn a semanteme of the training data, establishing an association between a user and the image data by using the text data as a medium, establishing a semantic association relationship between the image data and the user according to the semanteme of the training data and the association between the user and the image data, and establishing a user recommendation model according to the semantic association relationship, where the user recommendation model includes the semantic association relationship between the image data and the user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for establishing a user recommendation model in a social network comprising: obtaining training data from the social network, wherein the training data comprises text data, image data, and user-related data; performing heterogeneous data transfer learning on the training data to learn a semanteme of the training data; establishing a first association between a user and the image data by using the text data as a medium; establishing a semantic association relationship between the image data and the user according to the semanteme of the training data and the first association between the user and the image data; and establishing a user recommendation model according to the semantic association relationship, wherein the user recommendation model comprises the semantic association relationship between the image data and the user. 2. The method according to claim 1, wherein establishing the first association between the user and the image data by using the text data as the medium comprises: establishing a second association between the image data and the text data according to the training data; and establishing a third association between the user and the text data according to the user-related data. 3. The method according to claim 1, wherein performing the heterogeneous data transfer learning on the training data to learn the semanteme of the training data comprises performing heterogeneous data transfer learning on the training data by using covariance shift, multi-task learning, a sample TrAdaboost transfer learning method, a probabilistic latent semantic analysis (PLSA) algorithm, a principal component analysis (PCA) algorithm, a linear discriminant analysis (LDA) algorithm, a Bayesian model, a support vector machine, or a theme model to learn the semanteme of the training data. 4. A method for recommending a user in a social network comprising: obtaining related data of a target user, wherein the related data of the target user comprises at least image data; searching, by using a user recommendation model, for a user having a semantic association relationship with the image data of the target user, wherein the user recommendation model is established by performing heterogeneous data transfer learning on training data; and recommending a user corresponding to the semantic association relationship satisfying a preset condition to the target user when the semantic association relationship satisfies the preset condition. 5. The method according to claim 4, wherein recommending the user corresponding to the semantic association relationship satisfying the preset condition to the target user comprises pushing identifier data of the user to the target user. 6. An apparatus for establishing a user recommendation model in a social network comprising: a processor configured to: obtain training data from the social network, wherein the training data comprises text data, image data, and user-related data; perform heterogeneous data transfer learning on the training data, to learn a semanteme of the training data; establish a first association between a user and the image data by using the text data as a medium; establish a semantic association relationship between the image data and the user according to the semanteme of the training data and the first association between the user and the image data; and establish a user recommendation model according to the semantic association relationship, wherein the user recommendation model comprises the semantic association relationship between the image data and the user. 7. The apparatus according to claim 6, wherein the processor is further configured to establish a second association between the image data and the text data according to the training data; and establish a third association between the user and the text data according to the user-related data. 8. The apparatus according to claim 6, wherein the processor is further configured to perform heterogeneous data transfer learning on the training data by using covariance shift, multi-task learning, a sample TrAdaboost transfer learning method, a probabilistic latent semantic analysis (PLSA) algorithm, a principal component analysis (PCA) algorithm, a linear discriminant analysis (LDA) algorithm, a Bayesian model, a support vector machine, or a theme model to learn the semanteme of the training data. 9. An apparatus for recommending a user in a social network, comprising: a processor configured to: obtain related data of a target user, wherein the related data of the target user comprises at least image data; search, by using a user recommendation model, for a user having a semantic association relationship with the image data of the target user, wherein the user recommendation model is established by performing heterogeneous data transfer learning on training data; and recommend a user corresponding to the semantic association relationship satisfying a preset condition to the target user when the semantic association relationship satisfies the preset condition. 10. The apparatus according to claim 9, wherein the recommendation module is configured to push identifier data of the user to the target user. 11. A computer device, comprising: a processor; a memory configured to store a computer execution instruction; a bus coupling the memory to the processor; and a communications interface, wherein the processor executes the computer execution instruction stored in the memory to cause the processor to: obtain training data from the social network, wherein the training data comprises text data, image data, and user-related data; perform heterogeneous data transfer learning on the training data to learn a semanteme of the training data; establish a first association between a user and the image data by using the text data as a medium; establish a semantic association relationship between the image data and the user according to the semanteme of the training data and the first association between the user and the image data; and establish a user recommendation model according to the semantic association relationship, wherein the user recommendation model comprises the semantic association relationship between the image data and the user. 12. A computer device, comprising: a processor; a memory configured to store a computer execution instruction; a bus coupling the memory to the processor; and a communications interface, wherein the processor executes the computer execution instruction stored in the memory to cause the processor to: obtain related data of a target user, wherein the related data of the target user comprises at least image data; search, by using a user recommendation model, for a user having a semantic association relationship with the image data of the target user, wherein the user recommendation model is established by performing heterogeneous data transfer learning on training data; and recommend a user corresponding to the semantic association relationship satisfying a preset condition to the target user when the semantic association relationship satisfies the preset condition. 13. A computer readable medium comprising a computer execution instruction that when a processor of a computer executes the computer execution instruction, causes the processor to: obtain training data from the social network, wherein the training data comprises text data, image data, and user-related data; perform heterogeneous data transfer learning on the training data to learn a semanteme of the training data; establish a first association between a user and the image data by using the text data as a medium; establish a semantic association relationship between the image data and the user according to the semanteme of the training data and the first association between the user and the image data; and establish a user recommendation model according to the semantic association relationship, wherein the user recommendation model comprises the semantic association relationship between the image data and the user. 14. A computer readable medium comprising a computer execution instruction that when a processor of a computer executes the computer execution instruction, causes the processor to: obtain related data of a target user, wherein the related data of the target user comprises at least image data; search, by using a user recommendation model, for a user having a semantic association relationship with the image data of the target user, wherein the user recommendation model is established by performing heterogeneous data transfer learning on training data; and recommend a user corresponding to the semantic association relationship satisfying a preset condition to the target user when the semantic association relationship satisfies the preset condition.