Trust-based collaborative filtering: Tackling the cold start problem using regular equivalence

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation process such as explicit trust scores that are assigned by users to others or implicit trust relationships that result from social connections between users. Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust. In our work, we explore the use of regular equivalence applied to a trust network to generate a similarity matrix that is used to select the k-nearest neighbors for recommending items. We evaluate our approach on Epinions and we find that we can outperform related methods for tackling cold-start users in terms of recommendation accuracy.

Original languageEnglish
Title of host publicationRecSys 2018 - 12th ACM Conference on Recommender Systems
PublisherAssociation of Computing Machinery
Number of pages5
ISBN (Electronic)9781450359016
Publication statusPublished - 27 Sep 2018
Event12th ACM Conference on Recommender Systems: RecSys 2018 - Vancouver, Canada
Duration: 2 Oct 20187 Oct 2018


Conference12th ACM Conference on Recommender Systems


  • Cold-start
  • Collaborative Filtering
  • Katz similarity
  • Network Science
  • Recommender Systems
  • Regular Equivalence
  • Trust

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software


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