Post topic classification

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• determine that a user is interacting with an input post;
• access an input vector representing the input post, wherein:
• the input vector corresponds to a point in a vector space;
• the vector space comprises a plurality of clusters of training-page vectors that are each associated with a topic;
• each cluster was determined based on a clustering of a plurality of training-page vectors corresponding to a plurality of respective training pages that each comprise one or more training posts, wherein each training post was submitted by a user to a training page and comprises content selected by the user;
• and
• each training-page vector was generated by an artificial neural network (ANN) that was trained, based on the training posts of training pages associated with the ANN, to receive a post and then output, for each training page, a probability that the received post is related to the training posts of the training page;
• determine that the input vector representing the input post is located within a particular cluster in the vector space by calculating a similarity metric between the input vector and one or more training-page vectors in the vector space, wherein a similarity metric is calculated by measuring a distance between the input vector and one or more training-page vectors in the vector space;
• determine a topic of the input post based on the topic associated with the particular cluster that the input vector is located within;
• and
• provide, for display to the user, content associated with the topic of the input post.

A system comprising:

• one or more processors;
• and
• a memory coupled to the processors and comprising instructions operable when executed by the processors to cause the processors to:
• determine that a user is interacting with an input post;
• access an input vector representing the input post, wherein:
• the input vector corresponds to a point in a vector space;
• the vector space comprises a plurality of clusters of training-page vectors that are each associated with a topic;
• each cluster was determined based on a clustering of a plurality of training-page vectors corresponding to a plurality of respective training pages that each comprise one or more training posts, wherein each training post was submitted by a user to a training page and comprises content selected by the user;
• and
• each training-page vector was generated by an artificial neural network (ANN) that was trained, based on the training posts of training pages associated with the ANN, to receive a post and then output, for each training page, a probability that the received post is related to the training posts of the training page;
• determine that the input vector representing the input post is located within a particular cluster in the vector space by calculating a similarity metric between the input vector and one or more training-page vectors in the vector space, wherein a similarity metric is calculated by measuring a distance between the input vector and one or more training-page vectors in the vector space;
• determine a topic of the input post based on the topic associated with the particular cluster that the input vector is located within;
• and
• provide, for display to the user, content associated with the topic of the input post.