Attentive Graph-based Text-aware Preference Modeling for Top-N Recommendation

• URL: http://arxiv.org/abs/2305.12976v1
• Date: Mon, 22 May 2023 12:32:06 GMT
• Title: Attentive Graph-based Text-aware Preference Modeling for Top-N Recommendation
• Authors: Ming-Hao Juan, Pu-Jen Cheng, Hui-Neng Hsu and Pin-Hsin Hsiao
• Abstract summary: We propose a new model named Attentive Graph-based Text-aware Recommendation Model (AGTM) In this work, we aim to further improve top-N recommendation by effectively modeling both item textual content and high-order connectivity in user-item graph.
• Score: 2.3991565023534083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Textual data are commonly used as auxiliary information for modeling user preference nowadays. While many prior works utilize user reviews for rating prediction, few focus on top-N recommendation, and even few try to incorporate item textual contents such as title and description. Though delivering promising performance for rating prediction, we empirically find that many review-based models cannot perform comparably well on top-N recommendation. Also, user reviews are not available in some recommendation scenarios, while item textual contents are more prevalent. On the other hand, recent graph convolutional network (GCN) based models demonstrate state-of-the-art performance for top-N recommendation. Thus, in this work, we aim to further improve top-N recommendation by effectively modeling both item textual content and high-order connectivity in user-item graph. We propose a new model named Attentive Graph-based Text-aware Recommendation Model (AGTM). Extensive experiments are provided to justify the rationality and effectiveness of our model design.

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