Oath Inc.

Yahoo Assets LLC

Verizon Media Inc.

Troy Chevalier

Akshay Soni

Yashar Mehdad

Xue Wu

Parikshit Shah

Aasish Kumar Pappu

Kapil Raj Thadani

Rao Shen

Srikanth Nampelli

Ashwini Bhatkhande

Sreekanth Ramakrishnan

Aaron Nagao

Zhi Qu

Yue Ning

Pierce Yang

A method is provided, including: storing comments generated in response to a content item served over a network; analyzing the comments to determine features associated with each of the comments; using a scoring model to score each comment based on the comment's corresponding features; receiving a request to serve a subset of the comments; responsive to the request, selecting a ranking of the comments that is one permutation from possible rankings of the comments, wherein selecting the ranking is in accordance with a probability distribution of the possible rankings that is based on the scores of the comments; serving comments identified by the selected ranking over the network to a client device; determining a dwell time on the served comments; applying the dwell time to update the scoring model.

Ranking user comments on media using reinforcement learning optimizing for session dwell time

A method is provided, including: processing interactions by a plurality of users with a plurality of content items, the content items being provided over a network in response to user requests received over the network, wherein each content item is associated with one or more entities; for each user, determining a user entity set that includes entities associated with content items with which the user interacted; embedding the users and the entities in a vector space, wherein the embedding is configured to place a given user, and the entities of the given user's user entity set, in proximity to each other in the vector space; for each user, performing a proximity search in the vector space to identify a set of nearest entities to the user in the vector space; for each user, generating a user profile using the identified set of nearest entities to the user.

Collaborative personalization via simultaneous embedding of users and their preferences

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.

Latent user communities

Systems and methods are disclosed for discovering precursors associated with a current user interaction event. One method comprises receiving a selection of a new item by a user and determining a plurality of similarities between the new item selected by the user and a plurality of historical items, the plurality of historical items being associated with prior user activity. Then a plurality of importance weights associated with the plurality of historical items are determined. Based on the plurality of similarities and the plurality of importance weights, at least one enhanced importance matrix is generated. Further, prior interactions of the user with the plurality of historical items are determined. Based on the enhanced importance matrix and the prior interactions of the user with the plurality of historical items, precursors for the new item selected by the user are identified and provided to a display.

Analytical precursor mining for personalized recommendation

Software for a content-aggregation website generates a first representation of interests for a geographical location. The representation includes a plurality of entities that are derived from a corpus of documents. Each of the plurality of entities is associated with an expected value that is based on engagement signals from users in the geographical location and that is weighted using a sparse-polarity approach to be discriminative with respect to other entities. Each of the ingested articles is represented by the second representation that associates an aboutness score with each of the plurality of entities. The software uses the first representation, a similarity measure, and a second representation to create rankings of a plurality of ingested articles received. Then the software receives a request for access to the content-aggregation service from a new user from the geographical location and serves the new or infrequent user a content stream based on the rankings.

Baseline interest profile for recommendations using a geographic location

Software for a website hosting a content-aggregation service generates a first representation of interests for a user. The first representation includes a plurality of entities including pivot entities and extended entities, where the extended entities result from a nearest-neighbor search of word embeddings. Each of the extended entities is associated with a nearness score that is weighted using a distance of the extended entity from one of the pivot entities. For each of a plurality of articles ingested by the content-aggregation service, the software generates a second representation that associates an aboutness score with each of the plurality of entities. The software uses the first representation, a similarity measure, and the second representations to create rankings of the plurality of articles. The software receives a request for access to the content-aggregation service from the user and serves the user a content stream based at least in part on the rankings.

User profile expansion for personalization and recommendation

A method is provided, including: detecting interactions by a plurality of users with a plurality of content items, each content item having an associated content item vector; for a given user, identifying interactions occurring during a current time period, including identifying positive interactions with a first set of the content items, and negative interactions with a second set of the content items; processing a first set of the content item vectors that are associated with the first set of the content items to determine a positive interaction vector; processing a second set of the content item vectors that are associated to the second set of the content items to determine a negative interaction vector; for the given user, generating a current user profile vector for the current time period, using the positive interaction vector, the negative interaction vector, and a prior user profile vector for a prior time period.

Growing community of inventors

San Mateo, CA, United States of America

Troy Chevalier