Collaborative topic regression for online recommender systems: An online and Bayesian approach

• Main Authors: LIU, Chenghao, JIN, Tao, HOI, Steven C. H., ZHAO, Peilin, SUN, Jianling
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Topic modeling; Online learning; Recommender systems; Collaborative filtering; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3703; https://ink.library.smu.edu.sg/context/sis_research/article/4705/viewcontent/CollaborativeTopicRegressionOnlineRecomSys_2017.pdf
• Institution: Singapore Management University

Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has gained increasing success in many applications. Despite enjoying many advantages, the existing Batch Decoupled Inference algorithm for t...