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In recommender systems, multi-behavior methods have demonstrated their effectiveness in mitigating issues like data sparsity, a common challenge in traditional single-behavior recommendation approaches. These methods typically infer user preferences
Externí odkaz:
http://arxiv.org/abs/2404.18166
Collaborative filtering-based recommender systems that rely on a single type of behavior often encounter serious sparsity issues in real-world applications, leading to unsatisfactory performance. Multi-behavior Recommendation (MBR) is a method that s
Externí odkaz:
http://arxiv.org/abs/2306.10679
Multi-behavior recommendation exploits multiple types of user-item interactions to alleviate the data sparsity problem faced by the traditional models that often utilize only one type of interaction for recommendation. In real scenarios, users often
Externí odkaz:
http://arxiv.org/abs/2205.13128
Publikováno v:
In Knowledge-Based Systems 25 November 2023 280
Akademický článek
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