Abstract
Textual reviews of items are a popular resource of online recommendation. The semantic of reviews helps to achieve improved representation of users and items for recommendation. Current review-based recommender systems understand the semantic of reviews from a static view, i.e., independent of the specific user-item pair. However, the semantic of the reviews are personalized and context-aware, i.e., same reviews can have different semantics when they are written by different users or towards different items. Therefore, we propose an improved recommendation model by reconstructing multiple reviews into a personalized document. Given a user-item pair, we design a cross-attention model to build personalized documents by selecting important words in the reviews of the given user towards the given item and vice versa. A semantic encoder of personalized document is then designed using a cross-transformer mechanism to learn document-level representation of users and items. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model.
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Acknowledgement
This work was supported by a grant from the National Key Research and Development Program of China (2018YFC0809804), State Key Laboratory of Communication Content Cognition (Grant No. A32003), the Artificial Intelligence for Sustainable Development Goals (AI4SDGs) Research Program, National Natural Science Foundation of China (U1736103, 61976154, 61402323, 61876128), the National Key Research and Development Program (2017YFE0111900).
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Huang, Z., Wang, B., Liu, H., Jiang, Q., Xiong, N., Hou, Y. (2021). Improving Recommender System via Personalized Reconstruction of Reviews. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-92635-9_17
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DOI: https://doi.org/10.1007/978-3-030-92635-9_17
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