GLIMG: Global and Local Item Graphs for Top-N Recommender Systems

• URL: http://arxiv.org/abs/2007.14018v3
• Date: Wed, 11 Aug 2021 15:29:05 GMT
• Title: GLIMG: Global and Local Item Graphs for Top-N Recommender Systems
• Authors: Zhuoyi Lin, Lei Feng, Rui Yin, Chi Xu, and Chee-Keong Kwoh
• Abstract summary: We propose a novel graph-based recommendation model named GLIMG (Global and Local IteM Graphs) By integrating the global and local graphs into an adapted semi-supervised learning model, users' preferences on items are propagated globally and locally. Our proposed method consistently outperforms the state-of-the art counterparts on the top-N recommendation task.
• Score: 12.631785780195996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-based recommendation models work well for top-N recommender systems due to their capability to capture the potential relationships between entities. However, most of the existing methods only construct a single global item graph shared by all the users and regrettably ignore the diverse tastes between different user groups. Inspired by the success of local models for recommendation, this paper provides the first attempt to investigate multiple local item graphs along with a global item graph for graph-based recommendation models. We argue that recommendation on global and local graphs outperforms that on a single global graph or multiple local graphs. Specifically, we propose a novel graph-based recommendation model named GLIMG (Global and Local IteM Graphs), which simultaneously captures both the global and local user tastes. By integrating the global and local graphs into an adapted semi-supervised learning model, users' preferences on items are propagated globally and locally. Extensive experimental results on real-world datasets show that our proposed method consistently outperforms the state-of-the art counterparts on the top-N recommendation task.

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