Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Social information filtering: algorithms for automating “word of mouth”
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The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Recommender systems in e-commerce
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Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Developing trust in recommender agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Proceedings of the 10th international conference on Intelligent user interfaces
Automatic Content-Based Recommendation in e-Commerce
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Building security and trust in online banking
CHI '05 Extended Abstracts on Human Factors in Computing Systems
A trust-enhanced recommender system application: Moleskiing
Proceedings of the 2005 ACM symposium on Applied computing
Better control on recommender systems
CEC-EEE '06 Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services
A model of a trust-based recommendation system on a social network
Autonomous Agents and Multi-Agent Systems
User Participation in Social Media: Digg Study
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Trust-enhanced Security in Location-based Adaptive Authentication
Electronic Notes in Theoretical Computer Science (ENTCS)
Content-based personalised recommendation in virtual shopping environment
International Journal of Business Intelligence and Data Mining
Combining provenance with trust in social networks for semantic web content filtering
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
All the news that's fit to read: finding and recommending news online
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
Asknext: An agent protocol for social search
Information Sciences: an International Journal
The impact of recommender systems on item-, user-, and rating-diversity
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Trust based recommendation systems
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm
Information Systems Frontiers
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There has been a significant increase in interest and participation in social networking websites recently. For many users, social networks are indispensable tools for sharing personal information and keeping abreast with updates by their acquaintances. While there has been research on understanding the structure and effects of social networks, research on using social networks for developing targeted referral systems are few even though this can be valuable because of the abundance of information about user preferences, activities and choices. The goal of this research is to develop agent-based referral systems that learn user preferences based on past rating activities and caters to an individual user's interests by selectively searching the contributions posted by other users in close proximity in this user's social network. In particular, we are interested in fast notification of relevant activities in the social network that will enhance user awareness, satisfaction, and currency. In this paper, we propose keeping different trust values for a friend on different topics of interest and emphasize its importance with empirical results. We have developed an online photo referral system that identifies photos of possible interest to a user based on meta-data and comments on the pages of linked users on a popular photo sharing social website (flickr.com). We develop a probabilistic category determination mechanism that allows us to identify the possible categories an item belongs to by examining its tags. We use comments as an indirect measure of user preference for a photo. Empirical results show that our Social Network-based Item Recommendation (SNIR) system outperforms a content-based approach as well as the current recommendation schemes.