Streaming Recommender Systems
By Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark Hasegawa-Johnson, Thomas S. Huang. 2016
113 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research
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Citations in the corpus
- AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (2020, methodology): “…parameters and evaluate the proposed framework on examples in the pre-split test set; and (iii) following the streaming recommendation setting in [3], we also have offline parameter estimation and online inference stages, where we use historical user-item interactions to pre-train the AutoEmb’s…”