Roku, Inc.

Lian Liu

Fei Xiao

Abhishek Bambha

Rohit Mahto

Ronica Jethwa

Nam Vo

Zidong Wang

Jose Sanchez

Pulkit Aggarwal

Jing Ye

Khaldun Aidarabsah

Amit Verma

Rameen Mahdavi

Atishay Jain

Anirban Das

A set of content items can be accessed by a community of users having a set of interests. A set of interest based clusters for the set of content items correspond to the set of interests. For a user, a recommendation system can determine a group of user interest clusters selected from the set of interest based clusters. A popularity score for each content item of the set of content items with respect to the community of users can be generated, and an interest based popularity score for a content item within the interest based cluster can be generated based on a rank of the content item based on the popularity score of the content item. Recommendation candidates for the user can be generated based on the interest based popularity score of the content item for each content item in the group of user interest clusters.

Interest based recommendation system

Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for training a conversational recommendation system. An embodiment generates a probabilistic pseudo-user neural network model based on at least one interest probability distribution corresponding to a pseudo-user profile. The embodiment trains, using the pseudo-user neural network model, the conversational recommendation system to learn a recommendation policy, where the conversational recommendation system includes an interest-exploration engine and a prompt-decision engine. The training includes performing an iterative learning process that includes selecting an interest-exploration strategy based on one or more of the following: an interest-exploration policy, an earlier pseudo-user response generated by the pseudo-user neural network model, content data, and pseudo-user interaction history. The embodiment then generates, using the trained conversational recommendation system, a real-time recommendation having high play probability based on the minimal number of iterations of conversation between a user and the trained conversational recommendation system.

Interest-based conversational recommendation system

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating trailers (previews) for multimedia content. An example aspect operates by generating an initial set of candidate points to generate a trailer for a media content; determining conversion data for each of the initial set of candidate points; determining an updated set of candidate points based on the conversion data; determining an estimated mean and upper bound for each of the updated set of candidate points; computing a value for each of the updated set of candidate points; generating a ranked list based on the value computed for each of the updated set of candidate points; and repeating the process until an optimal candidate point is converged upon.

Automated trailer generation

Growing community of inventors

Rancho Palos Verdes, CA, United States of America

Lian Liu