Sequence-based trust for document recommendation

Hsuan Chiu*, Duen-Ren Liu, Chin Hui Lai

*Corresponding author for this work

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

Abstract

Collaborative Filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interests to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into the CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust value based on users' past ratings on items. A user is more trustworthy if he has contributed more accurate predictions than other users. Nevertheless, none of them derive the trust value based on a sequence of user's ratings on items. We propose a sequence-based trust model to derive the trust value based on users' sequences of ratings on documents. In knowledge-intensive environments, users normally have various information needs to access required documents over time, which forms a sequence of documents ordered according to their access time. The model considers two factors - time factor and document similarity in computing the trustworthiness of users. The proposed model is incorporated into standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method comparing to other trust-based recommender systems.

Original languageEnglish
Title of host publicationE-Commerce and Web Technologies - 10th International Conference, EC-Web 2009, Proceedings
Pages240-251
Number of pages12
DOIs
StatePublished - 4 Nov 2009
Event10th International Conference on E-Commerce and Web Technologies, EC-Web 2009 - Linz, Austria
Duration: 1 Sep 20094 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5692 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on E-Commerce and Web Technologies, EC-Web 2009
CountryAustria
CityLinz
Period1/09/094/09/09

Keywords

  • Collaborative Filtering
  • Recommender System
  • Sequence-based Trust

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