RaPare: A generic strategy for cold-start rating prediction problem

Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments, we propose a novel rating comparison strategy (RaPare ) to learn the latent profiles of cold-start users/items. The centerpiece of our RaPare is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RaPare strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario.

Original languageEnglish (US)
Article number7582500
Pages (from-to)1296-1309
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number6
StatePublished - Jun 1 2017


  • Recommender systems
  • cold-start problem
  • rating comparison strategy

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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