Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Rating news documents for similarity
Journal of the American Society for Information Science
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
An algorithm for automated rating of reviewers
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Proceedings of the 10th international conference on Intelligent user interfaces
The mechanics of trust: a framework for research and design
International Journal of Human-Computer Studies
Is trust robust?: an analysis of trust-based recommendation
Proceedings of the 11th international conference on Intelligent user interfaces
Improving collaborative filtering with trust-based metrics
Proceedings of the 2006 ACM symposium on Applied computing
Trust-enhanced visibility for personalized document recommendations
Proceedings of the 2006 ACM symposium on Applied computing
A familiar face(book): profile elements as signals in an online social network
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative Filtering Using Dual Information Sources
IEEE Intelligent Systems
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
International Journal of Human-Computer Studies
Q-rater: A collaborative reputation system based on source credibility theory
Expert Systems with Applications: An International Journal
Application of Agent-Based Personal Web of Trust to Local Document Ranking
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Using trust in collaborative filtering recommendation
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Source credibility model for neighbor selection in collaborative web content recommendation
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Novel personal and group-based trust models in collaborative filtering for document recommendation
Information Sciences: an International Journal
Hi-index | 0.00 |
Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users' past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users' various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users' sequences of ratings on documents. The model considers two factors - time factor and document similarity - in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a 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 in comparison with other trust-based recommender systems.