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

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.

OWA, hybrid approach, demographic- information, content-based filtering, collaborative filtering, recommender system