Adaptive information filtering for dynamic recommender systems
Ci-Hang Jin, Jian-Guo Liu, Yi-Cheng Zhang, Tao Zhou

TL;DR
This paper introduces two incremental diffusion algorithms for dynamic recommender systems that provide real-time personalized suggestions without cumulative errors, demonstrated on movie and social bookmarking datasets.
Contribution
The paper presents novel incremental diffusion-based algorithms that ensure fast, accurate, and non-cumulative error recommendations in dynamic environments.
Findings
Algorithms achieve fast response times.
Errors do not accumulate over time.
Effective on multiple real-world datasets.
Abstract
The dynamic environment in the real world calls for the adaptive techniques for information filtering, namely to provide real-time responses to the changes of system data. Where many incremental algorithms are designed for this purpose, they are usually challenged by the worse and worse performance resulted from the cumulative errors over time. In this Letter, we propose two incremental diffusion-based algorithms for the personalized recommendations, which integrate some pieces of local and fast updatings to achieve the approximate results. In addition to the fast responses, the errors of the proposed algorithms do not cumulate over time, that is to say, the global recomputing is unnecessary. This remarkable advantage is demonstrated by several metrics on algorithmic accuracy for two movie recommender systems and a social bookmarking system.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRecommender Systems and Techniques · Advanced Bandit Algorithms Research · Stochastic Gradient Optimization Techniques
