Addressing the New User Cold-Start Problem in Recommender Systems Using Ordered Weighted Averaging Operator

سال انتشار: 1389
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 170

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تاریخ نمایه سازی: 23 فروردین 1401

چکیده مقاله:

Recommender systems have become significant tools in electronic commerce, proposing effectively those items that best meet the preferences of users. A variety of techniques have been proposed for the recommender systems such as, collaborative filtering and content-based filtering. This study proposes a new hybrid recommender system that focuses on improving the performance under the "new user cold-start" condition where existence of users with no ratings or with only a small number of ratings is probable. In this method, the optimistic exponential type of ordered weighted averaging (OWA) operator is applied to fuse the output of five recommender system strategies. Experiments using MovieLens dataset show the superiority of the proposed hybrid approach in the cold-start conditions.


Javad Basiri

School of Electrical and Computer Engineering College of Engineering University of Tehran, Tehran, Iran

Azadeh Shakery

School of Electrical and Computer Engineering University of Tehran Tehran, Iran

Behzad Moshiri

Control & Intelligent Processing Center of Excellence, School of ECE University of Tehran Tehran, Iran

Morteza Zihayat

School of Electrical and Computer Engineering University of Tehran Tehran, Iran