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HNF: Hybrid Neural Filtering Based on Centrality-Aware Random Walk for Personalized Recommendation

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Social computing which analyzes users’ behaviors can help personalized recommender system to extract preferences of users. Most of personalized recommender systems exploit a user-item rating matrix to learn representations of… Click to show full abstract

Social computing which analyzes users’ behaviors can help personalized recommender system to extract preferences of users. Most of personalized recommender systems exploit a user-item rating matrix to learn representations of users and items for predicting users’ ratings on items. In this paper, we design a new framework, called HNF, to learn two kinds of representations and fuse them for recommendation. Our HNF consists of a topological neural filtering (TNF) module, collaborative neural filtering (CNF) module and prediction module. The TNF module is to learn topological representations of user-item interactions from a user-item bipartite graph constructed based on a user-item rating matrix. The CNF module is to learn collaborative representations of user-item interactions. The prediction module aims to fuse the topological representations and collaborative representations to generate hybrid representations for rating prediction. We conduct experiments on three real-world public datasets. Results validate that our proposed HNF algorithm outperforms the state-of-the-art algorithms in terms of higher evaluation metrics.

Keywords: recommendation; user item; hnf; neural filtering; module

Journal Title: IEEE Transactions on Network Science and Engineering
Year Published: 2022

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