Conference Proceeding Article
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
Recommender systems, Collaborative Filtering, Online learning, Multi-task Learning
Computer Sciences | Databases and Information Systems
Data Management and Analytics
RecSys'13: Proceedings of the 7th ACM Conference on Recommender Systems: October 12-16, 2013, Hong Kong, China
City or Country
WANG, Jialei; HOI, Steven C. H.; ZHAO, Peilin; and LIU, Zhi-Yong.
Online Multi-Task Collaborative Filtering for On-the-Fly Recommender Systems. (2013). RecSys'13: Proceedings of the 7th ACM Conference on Recommender Systems: October 12-16, 2013, Hong Kong, China. 237-244. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2334
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