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.
['Jialei Wang', 'Steven C. H. Hoi', 'Peilin Zhao', 'Zhiyong Liu']
Online multi-task collaborative filtering for on-the-fly recommender systems