ConsRec: Denoising Sequential Recommendation through User-Consistent Preference Modeling

• URL: http://arxiv.org/abs/2505.22130v1
• Date: Wed, 28 May 2025 08:55:13 GMT
• Title: ConsRec: Denoising Sequential Recommendation through User-Consistent Preference Modeling
• Authors: Haidong Xin, Qiushi Xiong, Zhenghao Liu, Sen Mei, Yukun Yan, Shi Yu, Shuo Wang, Yu Gu, Ge Yu, Chenyan Xiong,
• Abstract summary: We propose the User-Consistent Preference-based Sequential Recommendation System (ConsRec)<n>ConsRec captures stable user preferences and filters noisy items from interaction histories.<n>Results show ConsRec achieves a 13% improvement over baseline recommendation models.
• Score: 33.281526528724335
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: User-item interaction histories are pivotal for sequential recommendation systems but often include noise, such as unintended clicks or actions that fail to reflect genuine user preferences. To address this issue, we propose the User-Consistent Preference-based Sequential Recommendation System (ConsRec), designed to capture stable user preferences and filter noisy items from interaction histories. Specifically, ConsRec constructs a user-interacted item graph, learns item similarities from their text representations, and then extracts the maximum connected subgraph from the user-interacted item graph for denoising items. Experimental results on the Yelp and Amazon Product datasets illustrate that ConsRec achieves a 13% improvement over baseline recommendation models, showing its effectiveness in denoising user-interacted items. Further analysis reveals that the denoised interaction histories form semantically tighter clusters of user-preferred items, leading to higher relevance scores for ground-truth targets and more accurate recommendations. All codes are available at https://github.com/NEUIR/ConsRec.

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