ملخص
Collaborative filtering-based recommender systems are known to suffer from the item cold-start problem. Most recent attempts to mitigate this problem presented parametric approaches, such as deep content based models. In this paper, we show that a straightforward application of parametric models may lead to discrepancies between the cold and warm items' distributions in the CF space. As a remedy, we propose to combine parametric with non-parametric estimation for robust cold item placement. Extensive evaluation indicates that our method is competitive with other baselines, while producing cold items placement that better resembles the distribution of warm items in the collaborative filtering space.
اللغة الأصلية | الإنجليزيّة |
---|---|
الصفحات (من إلى) | 3260-3264 |
عدد الصفحات | 5 |
دورية | Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing |
مستوى الصوت | 2021-June |
المعرِّفات الرقمية للأشياء | |
حالة النشر | نُشِر - 2021 |
منشور خارجيًا | نعم |
الحدث | 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, كندا المدة: ٦ يونيو ٢٠٢١ → ١١ يونيو ٢٠٢١ |
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