Streaming Recommender Systems
This work addresses the challenge of real-time recommendation for streaming data, which is incremental as it builds on existing methods to handle high-velocity data streams.
The paper tackles the problem of recommendation with streaming data by proposing a principled framework, sRec, that models user and topic creation and interest evolution as continuous-time random processes, and demonstrates its advantages over state-of-the-art methods on real-world datasets.
The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and high-velocity, which poses tremendous challenges to traditional recommender systems. In this paper, we investigate the problem of recommendation with stream inputs. In particular, we provide a principled framework termed sRec, which provides explicit continuous-time random process models of the creation of users and topics, and of the evolution of their interests. A variational Bayesian approach called recursive meanfield approximation is proposed, which permits computationally efficient instantaneous on-line inference. Experimental results on several real-world datasets demonstrate the advantages of our sRec over other state-of-the-arts.