Roku, Inc.

Ratul Ray

Srimaruti Manoj Nimmagadda

Kapil Kumar

Nitish Aggarwal

Rahul Agarwal

Abhishek Majumdar

Danish Shaikh

Vinh Nguyen

Manasi Deshmukh

Fedor Bartosh

Peter Martigny

Sameer Girolkar

Thanh Dang

Yu Zhou

Yuzhong Li

Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for ranking a plurality of content items for presentation to a user. An embodiment generates a ranking score for each content item by: providing input to a deep machine learning (ML) model, the input including at least one or more query features and one or more content item features, determining, by the deep ML model and based at least on the input, a first probability of a first type of interaction between the user and the content item and a second probability of a second type of interaction between the user and the content item, and calculating the ranking score for the content item based at least on the first and second probabilities. An embodiment ranks the content items for presentation based on the ranking score associated with each content item.

Deep machine learning content item ranker

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for determining a list of recommended items in response to a user query. An embodiment can generate an ordered relevance list of items, and determine an initial reward value based on an array of relevance scores and an array of revenue values corresponding to the ordered relevance list of items, a parameter alpha assigned to the array of relevance scores, and a parameter beta assigned to the array of revenue values. The embodiment can generate a next list of recommended items from an initial list of recommended items, and further calculate a next reward value associated with the next list of recommended items, and determine a list of recommended items in response to the query based on a comparison of the initial reward value and the next reward value.

Search systems based on user relevance and revenue generation

A content retrieval system may receive a query associated with a plurality of content items in a repository. For each content item of the plurality of content items: a respective first and second similarity score may be generated based on a similarity between embeddings indicative of a first and second data type generated from the query and for the content item; and a respective normalized similarity score may be generated based on a combination of the respective first and second similarity scores. A set of content items with respective normalized similarity scores that satisfy a similarity score threshold may be identified. An exact-match (lexical) search may yield respective mapping scores for content items that may also be ranked. An output indicative of content items that are identified in the set of content items with high-ranking similarity scores and identified in the set of content items with high-ranking mapping scores.

Multimodal analysis for content item semantic retrieval and identification

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SANTA CLARA, CA, United States of America

Ratul Ray