RSOL: A trust-based recommender system with an opinion leadership measurement for cold start users

Jiun Yuan Wang, Hung-Yu Kao

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

1 Citation (Scopus)

Abstract

The cold start problem is a potential issue in computer-based information systems that involve a degree of automated data modeling. Specifically, the system cannot infer a rating for users or items that are new to the recommender system when no sufficient information has been gathered. Currently, more websites are providing the relationships between users, e.g., the trust relationships, to help us alleviate the cold start problem. In this paper, we proposed a trust-based recommender model (RSOL) that is able to recognize the user's recommendation quality for different items. A user's recommendation quality contains two parts: "Rating Confidence" - an indicator of the user's reliability when rating an item, and "Proximity Prestige" - an indicator of the user's influence on a trust network. In our experimental results, the proposed method outperforms the Collaborative Filtering and trust-based methods on the Epinions dataset.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 9th Asia Information Retrieval Societies Conference, AIRS 2013, Proceedings
Pages500-512
Number of pages13
DOIs
Publication statusPublished - 2013 Dec 1
Event9th Asia Information Retrieval Societies Conference on Information Retrieval Technology, AIRS 2013 - Singapore, Singapore
Duration: 2013 Dec 92013 Dec 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8281 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Asia Information Retrieval Societies Conference on Information Retrieval Technology, AIRS 2013
CountrySingapore
CitySingapore
Period13-12-0913-12-11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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