Online multi-task collaborative filtering for on-the-fly recommender systems

• Main Authors: WANG, Jialei, HOI, Steven C. H., ZHAO, Peilin, LIU, Zhi-Yong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Recommender systems; Collaborative Filtering; Online learning; Multi-task Learning; Computer Sciences; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/2334; https://ink.library.smu.edu.sg/context/sis_research/article/3334/viewcontent/Online_Multi_Task_Collaborative_Filtering_for_On_the_Fly_Recommender_Systems.pdf
• Institution: Singapore Management University

Traditional batch model-based Collaborative Filtering (CF) approaches typically assume a collection of users' rating data is given a priori for training the model. They suffer from a common yet critical drawback, i.e., the model has to be re-trained completely from scratch whenever new training data arrives, which is clearly non-scalable for large real recommender systems where users' rating data often arrives sequentially and frequently. In this paper, we investigate a novel efficient and scalable online collaborative filtering technique for on-the-fly recommender systems, which is able to effectively online update the recommendation model from a sequence of rating observations. Specifically, we propose a family of online multi-task collaborative filtering (OMTCF) algorithms, which tackle the online collaborative filtering task by exploiting the similar principle as online multitask learning. Encouraging empirical results on large-scale datasets showed that the proposed technique is significantly more effective than the state-of-the-art algorithms