Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Improving top-n recommendation techniques using rating variance
Proceedings of the 2008 ACM conference on Recommender systems
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Error-based collaborative filtering algorithm for top-N recommendation
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Social networks and interest similarity: the case of CiteULike
Proceedings of the 21st ACM conference on Hypertext and hypermedia
A social network-aware top-N recommender system using GPU
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
CrimeWalker: a recommendation model for suspect investigation
Proceedings of the fifth ACM conference on Recommender systems
An analysis of peer similarity for recommendations in P2P systems
Multimedia Tools and Applications
On top-k recommendation using social networks
Proceedings of the sixth ACM conference on Recommender systems
PRemiSE: personalized news recommendation via implicit social experts
Proceedings of the 21st ACM international conference on Information and knowledge management
An empirical comparison of social, collaborative filtering, and hybrid recommenders
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Collaborative filtering by analyzing dynamic user interests modeled by taxonomy
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Nearest neighbour based social recommendation using heat diffusion
Proceedings of the 6th ACM India Computing Convention
Personalized news recommendation via implicit social experts
Information Sciences: an International Journal
A survey of collaborative filtering based social recommender systems
Computer Communications
Hi-index | 0.00 |
Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended for top-N recommendation, but this approach does not work accurately for cold start users that have rated only a very small number of items. In this paper we propose novel methods exploiting a trust network to improve the quality of top-N recommendation. The first method performs a random walk on the trust network, considering the similarity of users in its termination condition. The second method combines the collaborative filtering and trust-based approach. Our experimental evaluation on the Epinions dataset demonstrates that approaches using a trust network clearly outperform the collaborative filtering approach in terms of recall, in particular for cold start users.