Cold Start revisited: A deep hybrid recommender with cold-warm item harmonization

Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, Yoni Weill, Noam Koenigstein

نتاج البحث: نشر في مجلةمقالة من مؤنمرمراجعة النظراء


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 الأصليةالإنجليزيّة
الصفحات (من إلى)3260-3264
عدد الصفحات5
دوريةProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
مستوى الصوت2021-June
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2021
منشور خارجيًانعم
الحدث2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, كندا
المدة: ٦ يونيو ٢٠٢١١١ يونيو ٢٠٢١

ملاحظة ببليوغرافية

Publisher Copyright:
© 2021 IEEE


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