2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-9.12
Paper Title Cross-Domain Recommendation Method Based on Multi-layer Graph Analysis with Visual Information
Authors Taisei Hirakawa, Keisuke Maeda, Takahiro Ogawa, Hokkaido University, Japan; Satoshi Asamizu, National Institute of Technology, Kushiro College, Japan; Miki Haseyama, Hokkaido University, Japan
SessionARS-9: Interpretation, Understanding, Retrieval
LocationArea I
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Storage and Retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents a cross-domain recommendation (CDR)method based on multi-layer graph analysis with visual information. Although previous graph-based CDR methods have effectively used users' ratings of products and their purchase histories, they lack essential information such as product images that can have an impact on users' decision of whether to purchase products. Then the proposed method newly introduces visual features obtained from product images into the graph-based CDR. For dealing with visual features in multiple domains, we focus on both intra-domain and inter-domain relationships through visual features. Specifically, to obtain effective embedding features from users and items in a domain, the proposed method newly introduces visual features into an optimization process of the latest graph neural network that considers only user-item interactions. Consideration of the visual similarity of items within a domain becomes feasible, and embedding features with high representation ability can be estimated. Furthermore, to consider visual information between domains, we construct multi-layer graphs for each domain and introduce visual features into the training of a feature transformer across these graphs. Therefore, consideration of both intra-domain and inter-domain relationships through visual features contributes to the performance improvement. To our best knowledge, this is the first trial to introduce visual features into multi-layer graph-based CDR. The effectiveness of our method is demonstrated by comparing several state-of-the-art methods.