A Novel Forgetting Technique with Random Walk Sampling for Scalable and Adaptive Stream-Based Recommender Systems

Murilo F. L. Schmitt; Eduardo J. Spinosa

doi:10.5753/jbcs.2026.5496

Authors

Murilo F. L. Schmitt State University of the Midwest of Paraná and Federal University of Paraná https://orcid.org/0000-0002-7758-5778
Eduardo J. Spinosa Federal University of Paraná https://orcid.org/0000-0003-4438-6198

DOI:

https://doi.org/10.5753/jbcs.2026.5496

Keywords:

Recommender Systems, Data Streams, Random Walks, Forgetting, Online Learning

Abstract

The explosion of user-generated data at fast rates in online services leads to the need for designing scalable recommender systems that are able to learn from data streams. Stream-based recommender systems are specifically devised for these scenarios, and have seen a recent increase in interest. These systems rely on incremental approaches that incorporate newly generated data on a single pass, resulting in a model that is always up-to-date. A known limitation of only incorporating data into a model is the presence and effect of old data, which negatively affects predictive performance and eventually raises scalability issues. Therefore, an explicit mechanism to forget such data and remove it from the model is required. In this work, we present a graph-based recommender system that recommends items based on random walk sampling, and simultaneously includes new information while also forgetting obsolete ones. Information obtained from random walk sampling is not only used to recommend relevant items, but also to capture structural information from the graph. We devise a forgetting function that prunes obsolete edges based on this information, and also on the recency, popularity and acceptance ratio of items. Our experiments highlight the importance of forgetting obsolete information and suggest the effectiveness of our method, which leads to scalability, accuracy and diversity improvements.

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