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Multimodal collaborative graph for image recommendation
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AI
DOI：10.1007/s10489-022-03304-x.png)
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Abstract
En 中文
Recent works for personalized recommendation typically emphasize their efforts on learning users' interests from interactions. However, users make decisions depending on multiple factors, especially various attributes of items like appearance, reviews, price, etc. Therefore, in the case of image recommendation, we strive to unveil users' interests in a multimodal manner. In this work, we propose a multimodal collaborative graph (MCG) model for image recommendation, which builds users' interests in both visual and collaborative signals. On visual modality, visual interest filtering is designed to explore the interest non-linearity of users' interacted images. In the pairwise collaborative module, multi-hop interactions are embedded elaborately to encode the heterogeneous structure of user-image interactions by deep interest propagation. Both visual and collaborative signals are aggregated to embed users and items and match pairwise user-item for the following personalized recommendation. Experiments are conducted on three public real-world datasets. Further analysis demonstrates the compensation capability of visual and collaborative signals in mining users' interests and verifies the effectiveness of the proposed MCG for image recommendation.
Keywords:
Image recommendation
Multimodal collaboration
User interest
Graph neural network
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