Produção Nacional
Revisado por pares
Graph-based Recommendation Meets Bayes and Similarity Measures
2019; Association for Computing Machinery; Volume: 11; Issue: 1 Linguagem: Inglês
10.1145/3356882
ISSN2157-6912
AutoresRamon Lopes, Renato Assunção, Rodrygo L. T. Santos,
Tópico(s)Caching and Content Delivery
ResumoGraph-based approaches provide an effective memory-based alternative to latent factor models for collaborative recommendation. Modern approaches rely on either sampling short walks or enumerating short paths starting from the target user in a user-item bipartite graph. While the effectiveness of random walk sampling heavily depends on the underlying path sampling strategy, path enumeration is sensitive to the strategy adopted for scoring each individual path. In this article, we demonstrate how both strategies can be improved through Bayesian reasoning. In particular, we propose to improve random walk sampling by exploiting distributional aspects of items’ ratings on the sampled paths. Likewise, we extend existing path enumeration approaches to leverage categorical ratings and to scale the score of each path proportionally to the affinity of pairs of users and pairs of items on the path. Experiments on several publicly available datasets demonstrate the effectiveness of our proposed approaches compared to state-of-the-art graph-based recommenders.
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