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pro vyhledávání: '"Tokutake, Yu"'
Autor:
Tokutake, Yu, Okamoto, Kazushi
Publikováno v:
Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 2024
Serendipity-oriented recommender systems aim to counteract over-specialization in user preferences. However, evaluating a user's serendipitous response towards a recommended item can be challenging because of its emotional nature. In this study, we a
Externí odkaz:
http://arxiv.org/abs/2404.07499
In this paper, we propose a solution that won the 10th prize in the KDD Cup 2023 Challenge Task 2 (Next Product Recommendation for Underrepresented Languages/Locales). Our approach involves two steps: (i) Identify candidate item sets based on co-visi
Externí odkaz:
http://arxiv.org/abs/2310.07281