Adobe, Inc.

Lakshmi Shivalingaiah

Viswanathan Swaminathan

Anup Bandigadi Rao

Saayan Mitra

Sumit Shekhar

Tung Mai

Somdeb Sarkhel

Charles Menguy

Matvey Kapilevich

Dwight O Rodgers

Payal Bajaj

Margarita Savova

Moumita Sinha

George Horia Galatanu

Jason Boyer

Vijeth Lomada

Harleen S Sahni

Shuo Yang

Liron Goren Snai

Multi-modal machine-learning model training techniques for search are described that overcome conventional challenges and inefficiencies to support real time output, which is not possible in conventional training techniques. In one example, a search system is configured to support multi-modal machine-learning model training. This includes use of a preview mode and an expanded mode. In the preview mode, a preview segment is generated as part of real time training of a machine learning model. In the expanded mode, the preview segment is persisted as an expanded segment that is used to train and utilize an expanded machine-learning model as part of search.

Multi-modal machine-learning model training for search

This disclosure relates to methods, non-transitory computer readable media, and systems that determine multiple personas corresponding to a user account for digital content and train a persona classifier to predict a given persona (from among the multiple personas) for content requests associated with the user account. By using the persona classifier, the disclosed methods, non-transitory computer readable media, and systems accurately detect a given persona for a content request upon initiation of the request. Based on determining the given persona, in some implementations, the methods, non-transitory computer readable media, and systems generate a digital-content recommendation for presentation on a client device associated with the user account.

Predicting digital personas for digital-content recommendations using a machine-learning-based persona classifier

Various embodiments disambiguate users who share media content accounts to provide persona-based experience individualization. Personas correspond to commonly observed channel watching patterns among media content customers. Decomposition of the media content account into personas is achieved by analyzing many accounts, e.g., millions of accounts, on media content platforms. By analyzing accounts, a recommendation system can individualize the channel watching experience in media content accounts.

Decomposing media content accounts for persona-based experience individualization

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Berkeley, CA, United States of America

Lakshmi Shivalingaiah