From ratings to trust: An empirical study of implicit trust in recommender systems

Guibing Guo, Jie Zhang, Daniel Thalmann, Anirban Basu, Neil Yorke-Smith

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

44 Citations (Scopus)

Abstract

Trust has been extensively studied and its effectiveness demonstrated in recommender systems. Due to the lack of explicit trust information in most systems, many trust metric approaches have been proposed to infer implicit trust from user ratings. However, previous works have not compared these different approaches, and oftentimes focus only on the performance of predictive item ratings. In this paper, we first analyse five kinds of trust metrics in light of the properties of trust. We conduct an empirical study to explore the ability of trust metrics to distinguish explicit trust from implicit trust and to generate accurate predictions. Experimental results on two real-world data sets show that existing trust metrics cannot provide satisfying performance, and indicate that future metrics should be designed more carefully.

Original languageEnglish
Title of host publicationProceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014
PublisherAssociation for Computing Machinery (ACM)
Pages248-253
Number of pages6
ISBN (Print)9781450324694
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event29th Annual ACM Symposium on Applied Computing, SAC 2014 - Gyeongju, Korea, Republic of
Duration: 24 Mar 201428 Mar 2014

Conference

Conference29th Annual ACM Symposium on Applied Computing, SAC 2014
CountryKorea, Republic of
CityGyeongju
Period24/03/1428/03/14

Keywords

  • Ratings
  • Recommender systems
  • Similarity
  • Trust metrics

Fingerprint Dive into the research topics of 'From ratings to trust: An empirical study of implicit trust in recommender systems'. Together they form a unique fingerprint.

Cite this