Online collaborative filtering with local and global consistency

Collaborative Filtering (CF) is one of the most popular technologies used in online recommendation systems. Most of the existing CF studies focus on the offline algorithms, a major drawback of these algorithms is the lack of ability to use the latest user feedbacks to update the learned model in realtime, due to the high cost of the offline training procedure. In this work, we propose Logo, an online CF algorithm. Our proposed method is based on a hierarchical generative model, with which, we derive a set of local and global consistency constraints for the prediction targets, and eventually obtain the design of the learning algorithm. We conduct comprehensive experiments to evaluate the proposed algorithm, the results show that: (1) Under the online setting, our algorithm achieves notably better prediction results than the benchmark algorithms; (2) Under the offline setting, our algorithm attains comparable accurate prediction results with the best performed competitors; (3) In all the experiments, our algorithm performs tens or even hundreds of times faster than the comparison algorithms.