Streaming Recommender Systems

TL;DR

This paper introduces sRec, a streaming recommender system framework that models user and topic evolution over time using continuous-time random processes, enabling efficient real-time recommendations.

Contribution

The paper presents a novel framework, sRec, with a variational Bayesian approach for online inference in streaming recommender systems, addressing challenges of high-velocity data.

Findings

sRec outperforms existing methods on real-world datasets

Efficient online inference via recursive meanfield approximation

Effective modeling of user and topic evolution over time

Abstract

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.