Cold item recommendations via hierarchical Item2vec

Oren Barkan, Avi Caciularu, Idan Rejwan, Ori Katz, Jonathan Weill, Itzik Malkiel, Noam Koenigstein

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء


Learning item representations is a key building block in recommender systems research. However, representations often suffer from the cold start problem - a well-known problem in which rare items in the tail of the distribution face insufficient data yielding inadequate representations. In this work, we present a novel hybrid recommender that supports the utilization of hierarchical content-based information to mitigate the cold start problem. In particular, we assume a taxonomy of item tags in which every item is associated with several 'parent' tags and the tags themselves can be associated with several 'parent' tags in a hierarchical manner. Our model learns item representations that are guided by the 'parent' tags of each item which allows propagating relevant information between items sharing the same hierarchy. In addition, the tags are modeled using tag representations that allow propagating information between any two tags that share a common ancestor. Due to space limitation, we focus this work on a recommendations task, however the same approach can be utilized for general representation learning e.g. language models.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفProceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
المحررونClaudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
ناشرInstitute of Electrical and Electronics Engineers Inc.
عدد الصفحات6
رقم المعيار الدولي للكتب (الإلكتروني)9781728183169
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - نوفمبر 2020
منشور خارجيًانعم
الحدث20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, إيطاليا
المدة: ١٧ نوفمبر ٢٠٢٠٢٠ نوفمبر ٢٠٢٠

سلسلة المنشورات

الاسمProceedings - IEEE International Conference on Data Mining, ICDM
مستوى الصوت2020-November
رقم المعيار الدولي للدوريات (المطبوع)1550-4786


!!Conference20th IEEE International Conference on Data Mining, ICDM 2020
المدينةVirtual, Sorrento

ملاحظة ببليوغرافية

Publisher Copyright:
© 2020 IEEE.


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