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
Fab: content-based, collaborative recommendation
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Scale and Translation Invariant Collaborative Filtering Systems
Information Retrieval
Proceedings of the 10th international conference on Intelligent user interfaces
An MDP-Based Recommender System
The Journal of Machine Learning Research
Improving collaborative filtering with trust-based metrics
Proceedings of the 2006 ACM symposium on Applied computing
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Investigating interactions of trust and interest similarity
Decision Support Systems
A user-oriented contents recommendation system in peer-to-peer architecture
Expert Systems with Applications: An International Journal
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
A time-based approach to effective recommender systems using implicit feedback
Expert Systems with Applications: An International Journal
Individual and group behavior-based customer profile model for personalized product recommendation
Expert Systems with Applications: An International Journal
An iterative semi-explicit rating method for building collaborative recommender systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Personalized Recommendation over a Customer Network for Ubiquitous Shopping
IEEE Transactions on Services Computing
Social Trust-Aware Recommendation System: A T-Index Approach
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
FeedbackTrust: using feedback effects in trust-based recommendation systems
Proceedings of the third ACM conference on Recommender systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Mobile commerce product recommendations based on hybrid multiple channels
Electronic Commerce Research and Applications
Trust based recommender system using ant colony for trust computation
Expert Systems with Applications: An International Journal
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Information Sciences: an International Journal
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Electronic Commerce Research and Applications
| Hi-index | 12.05 |
Traditional collaborative filtering (CF) based recommender systems on the basis of user similarity often suffer from low accuracy because of the difficulty in finding similar users. Incorporating trust network into CF-based recommender system is an attractive approach to resolve the neighbor selection problem. Most existing trust-based CF methods assume that underlying relationships (whether inferred or pre-existing) can be described and reasoned in a web of trust. However, in online sharing communities or e-commerce sites, a web of trust is not always available and is typically sparse. The limited and sparse web of trust strongly affects the quality of recommendation. In this paper, we propose a novel method that establishes and exploits a two-faceted web of trust on the basis of users' personal activities and relationship networks in online sharing communities or e-commerce sites, to provide enhanced-quality recommendations. The developed web of trust consists of interest similarity graphs and directed trust graphs and mitigates the sparsity of web of trust. Moreover, the proposed method captures the temporal nature of trust and interest by dynamically updating the two-faceted web of trust. Furthermore, this method adapts to the differences in user rating scales by using a modified Resnick's prediction formula. As enabled by the Pareto principle and graph theory, new users highly benefit from the aggregated global interest similarity (popularity) in interest similarity graph and the global trust (reputation) in the directed trust graph. The experiments on two datasets with different sparsity levels (i.e., Jester and MovieLens datasets) show that the proposed approach can significantly improve the predictive accuracy and decision-support accuracy of the trust-based CF recommender system.