BACKGROUND

The present disclosure relates to methods and systems for determining latent user communities based on online activity.

Community based recommendation of content is an interesting and active area of research. Such recommendations often drive user engagement with the platform of discussion (e.g., Reddit, Yahoo! Newsroom). Existing platforms of discussion such as Reddit, Digg, and other moderated forums do not leverage the temporal dynamics of a platform to improve topic/community recommendation for existing or new users.

It is in this context that implementations of the disclosure arise.

SUMMARY

Community based recommendation of content is an interesting and active area of research, as such recommendations often drive user engagement with the platform of discussion (e.g., Reddit). Implementations in accordance with the present disclosure provide a temporally-dynamic community-driven exploration method for users. Latent social network dynamics are analyzed to enable grouping of topics (e.g. “Vibes” in Yahoo! Newsroom) into high-level communities. These communities are latent in nature because they reflect underlying social connections that bind the users. It is possible to model these connections by observing community activity (e.g., volume of posts, social signals, topical distribution of comments) triggered by real-life events. A community can be considered as a cluster or collection of topics where a new topic can be added or removed from a given community as time progresses. Such modeling effectively captures the dynamics of an ever-evolving social network. The community can have a label assigned by the editors or even by the users themselves.

By understanding user behavior through identification of latent communities, it is possible to provide community-based content recommendation solutions. Broadly, this could mean several things, for example: (a) recommending specific posts, (b) recommending topics in strongly connected communities that the user is following, and (c) allowing users to follow communities (dynamic sets of changing topics). The latter of these is significant in that there is no existing notion of collections of related topics that are generated from latent community identification as in implementations of the present disclosure.

Additionally, implementations of the present disclosure enable modeling and visualization of the latent communities that make up the users and topics. This can be used both for informational dashboards and exploratory analysis.

In some implementations, a method implemented by at least one server computer is provided, including: providing, over the Internet, access to a plurality of topics, wherein each topic includes, and further provides access to, a plurality of posted items; recording interaction data for the plurality of topics, the interaction data identifying user activity occurring within each of the topics; analyzing the interaction data to identify clusters of topics that exhibit similar behavioral patterns; for each cluster of topics, generating a community that includes the topics in the cluster; providing, over the Internet, access to the communities, wherein accessing a given community further provides access to the topics included in that community, which further provide access to the posted items that are included in the topics within that community.

In some implementations, analyzing the interaction data includes: determining a covariance amongst the plurality of topics using the interaction data; generating a sparse graph using the determined covariance; processing the sparse graph to identify the clusters of topics that exhibit similar behavioral patterns.

In some implementations, determining the covariance amongst the plurality of topics includes generating a covariance matrix using the interaction data; wherein generating the sparse graph includes applying a graphical lasso to the covariance matrix; wherein processing the sparse graph includes applying a stochastic block model to the sparse graph.

In some implementations, the interaction data defines a time-dependent metric for user activity within the plurality of topics, and wherein the similar behavioral pattern for a given cluster of topics is defined by similar changes in the time-dependent metric occurring over time for the given cluster of topics.

In some implementations, the interaction data includes a number of one or more of active users, posted items, comments, votes, positive endorsements, negative endorsements, flags, moderator actions.

In some implementations, providing access to a given topic includes providing an interface for posting items within the given topic, and for interacting with posted items in the given topic, and wherein the interaction data includes data obtained via the interface.

In some implementations, the user-posted items include news articles.

In some implementations, a method implemented by at least one server computer is provided, including: providing, over the Internet, access to a plurality of topics, wherein each topic includes, and further provides access to, a plurality of posted items; recording interaction data for the plurality of topics, the interaction data identifying user activity occurring within each of the topics; analyzing the interaction data to identify clusters of topics that exhibit similar behavioral patterns; using the identified clusters of topics to identify topics for recommendation to a user; presenting, over the Internet, the topics identified for recommendation in a session for the user.

In some implementations, the topics for recommendation are identified based on identifying a topic to which the user has subscribed, and identifying further topics that are within a same cluster as the topic to which the user has subscribed.

In some implementations, analyzing the interaction data includes: determining a covariance amongst the plurality of topics using the interaction data; generating a sparse graph using the determined covariance; processing the sparse graph to identify the clusters of topics that exhibit similar behavioral patterns.

In some implementations, determining the covariance amongst the plurality of topics includes generating a covariance matrix using the interaction data; wherein generating the sparse graph includes applying a graphical lasso to the covariance matrix; wherein processing the sparse graph includes applying a stochastic block model to the sparse graph to generate clusters.

In some implementations, the interaction data defines a time-dependent metric for user activity within the plurality of topics, and wherein the similar behavioral pattern for a given cluster of topics is defined by similar changes in the time-dependent metric occurring over time for the given cluster of topics.

In some implementations, the interaction data includes a number of one or more of active users, posted items, comments, votes, positive endorsements, negative endorsements, abuse flags, moderator actions.

In some implementations, providing access to a given topic includes providing an interface for posting items within the given topic, and for interacting with posted items in the given topic, and wherein the interaction data includes data obtained via the interface.

In some implementations, the user-posted items include news articles.

In some implementations, a method implemented by at least one server computer is provided, including: providing, over the Internet, access to a plurality of topics, wherein each topic includes, and further provides access to, a plurality of posted items; recording interaction data for the plurality of topics, the interaction data identifying user activity occurring within each of the topics; analyzing the interaction data to identify clusters of topics that exhibit similar behavioral patterns; using the identified clusters to recommend specific user-posted items.

In some implementations, using the identified clusters to recommend specific user-posted items further includes, identifying a first topic to which a user has subscribed, identifying a second topic that is within a same cluster as the first topic, and recommending in a session of the user a posted item from the second topic.

In some implementations, analyzing the interaction data includes: determining a covariance amongst the plurality of topics using the interaction data; generating a sparse graph using the determined covariance; processing the sparse graph to identify the clusters of topics that exhibit similar behavioral patterns.

In some implementations, determining the covariance amongst the plurality of topics includes generating a covariance matrix using the interaction data; wherein generating the sparse graph includes applying a graphical lasso to the covariance matrix; wherein processing the sparse graph includes applying a stochastic block model to the sparse graph.

In some implementations, the interaction data defines a time-dependent metric for user activity within the plurality of topics, and wherein the similar behavioral pattern for a given cluster of topics is defined by similar changes in the time-dependent metric occurring over time for the given cluster of topics.

Other aspects of the disclosure will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the disclosure.

