TY - GEN
T1 - A gradient-based adaptive learning framework for efficient personal recommendation
AU - Ning, Yue
AU - Shi, Yue
AU - Hong, Liangjie
AU - Rangwala, Huzefa
AU - Ramakrishnan, Naren
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/8/27
Y1 - 2017/8/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85030480257&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030480257&partnerID=8YFLogxK
U2 - 10.1145/3109859.3109909
DO - 10.1145/3109859.3109909
M3 - Conference contribution
AN - SCOPUS:85030480257
T3 - RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems
SP - 23
EP - 31
BT - RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems
T2 - 11th ACM Conference on Recommender Systems, RecSys 2017
Y2 - 27 August 2017 through 31 August 2017
ER -