Considering Fine-Grained and Coarse-Grained Information for Context-Aware Recommendations. (4th August 2020)

• Record Type: Journal Article
• Authors: Luo, Yiqin; Sun, Yanpeng; Chang, Liang; Gu, Tianlong; Bin, Chenzhong; Li, Long
• Is Part Of: Computer journal. Volume 65:Number 3(2022)
• Page Start: 679
• Page End: 688
• Publication Date: 2020-08-04
• Subjects: attention mechanism -- self-attention mechanism -- fine-grained and coarse-grained framework -- context-aware recommendations; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa095
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: In context-aware recommendation systems, most existing methods encode users' preferences by mapping item and category information into the same space, which is just a stack of information. The item and category information contained in the interaction behaviours is not fully utilized. Moreover, since users' preferences for a candidate item are influenced by the changes in temporal and historical behaviours, it is unreasonable to predict correlations between users and candidates by using users' fixed features. A fine-grained and coarse-grained information based framework proposed in our paper which considers multi-granularity information of users' historical behaviours. First, a parallel structure is provided to mine users' preference information under different granularities. Then, self-attention and attention mechanisms are used to capture the dynamic preferences. Experiment results on two publicly available datasets show that our framework outperforms state-of-the-art methods across the calculated evaluation metrics.