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Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning.

Authors :
Zhu, Yu
Lin, Jinghao
He, Shibi
Wang, Beidou
Guan, Ziyu
Liu, Haifeng
Cai, Deng
Source :
IEEE Transactions on Knowledge & Data Engineering. Apr2020, Vol. 32 Issue 4, p631-644. 14p.
Publication Year :
2020

Abstract

In recommender systems, cold-start issues are situations where no previous events, e.g., ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g., item attributes) and initial user ratings are valuable for seizing users’ preferences on a new item. However, previous methods for the item cold-start problem either (1) incorporate content information into collaborative filtering to perform hybrid recommendation, or (2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leveraging both active learning and items’ attribute information. Specifically, we design useful user selection criteria based on items’ attributes and users’ rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users’ previous ratings and items’ attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10414347
Volume :
32
Issue :
4
Database :
Academic Search Index
Journal :
IEEE Transactions on Knowledge & Data Engineering
Publication Type :
Academic Journal
Accession number :
143313724
Full Text :
https://doi.org/10.1109/TKDE.2019.2891530