A gradient-based adaptive learning framework for efficient personal recommendation

Yue Ning, Yue Shi, Liangjie Hong, Huzefa Rangwala, Naren Ramakrishnan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Recommending personalized content to users is a long-standing challenge to many online services including Facebook, Yahoo, Linkedin and Twitter. Traditional recommendation models such as latent factor models and feature-based models are usually trained for all users and optimize an "average" experience for them, yielding sub-optimal solutions. Although multi-task learning provides an opportunity to learn personalized models per user, learning algorithms are usually tailored to specific models (e.g., generalized linear model, matrix factorization and etc.), creating obstacles for a unified engineering interface, which is important for large Internet companies. In this paper, we present an empirical framework to learn user-specific personal models for content recommendation by utilizing gradient information from a global model. Our proposed method can potentially benefit any model that can be optimized through gradients, offering a lightweight yet generic alternative to conventional multi-task learning algorithms for user personalization. We demonstrate the effectiveness of the proposed framework by incorporating it in three popular machine learning algorithms including logistic regression, gradient boosting decision tree and matrix factorization. Our extensive empirical evaluation shows that the proposed framework can significantly improve the efficiency of personalized recommendation in real-world datasets.

Original languageEnglish
Title of host publicationRecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems
Pages23-31
Number of pages9
ISBN (Electronic)9781450346528
DOIs
StatePublished - 27 Aug 2017
Event11th ACM Conference on Recommender Systems, RecSys 2017 - Como, Italy
Duration: 27 Aug 201731 Aug 2017

Publication series

NameRecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems

Conference

Conference11th ACM Conference on Recommender Systems, RecSys 2017
Country/TerritoryItaly
CityComo
Period27/08/1731/08/17

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