Can you trust the user? Collaborative trust estimation model for recommendations

Daichi Minami, Taketoshi Ushiama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The explosive popularity of social networking services such as Twitter and Facebook has made it common to communicate with unknown people via the Internet. Further, services based on a 'sharing economy' such as Airbnb and Uber have gained popularity and increased opportunities to connect with strangers. Users would like to determine whether the opinions of other users can be trusted. However, it is sometimes difficult to do so, and the judgment of other users is not necessarily correct. We propose a model for predicting the trust of unknown users by using information of users known to a target user. We predicted the trust of reviewers on an online review site based on the proposed model and recommended items based on collaborative filtering (CF) using the obtained trust of the reviewers. The experimental results showed that the recommendation accuracy with the trust was higher than that with general CF.

Original languageEnglish
Title of host publication2017 12th International Conference on Digital Information Management, ICDIM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages252-256
Number of pages5
ISBN (Electronic)9781538606643
DOIs
Publication statusPublished - Jun 28 2017
Event12th International Conference on Digital Information Management, ICDIM 2017 - Fukuoka, Japan
Duration: Sep 12 2017Sep 14 2017

Publication series

Name2017 12th International Conference on Digital Information Management, ICDIM 2017
Volume2018-January

Other

Other12th International Conference on Digital Information Management, ICDIM 2017
CountryJapan
CityFukuoka
Period9/12/179/14/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Fingerprint Dive into the research topics of 'Can you trust the user? Collaborative trust estimation model for recommendations'. Together they form a unique fingerprint.

Cite this