An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
How do people manage their digital photographs?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 10th international conference on Intelligent user interfaces
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing 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
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Tagsplanations: explaining recommendations using tags
Proceedings of the 14th international conference on Intelligent user interfaces
EnTag: enhancing social tagging for discovery
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Identifying influential reviewers for word-of-mouth marketing
Electronic Commerce Research and Applications
Improving tag recommendation using social networks
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
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Trust-ware recommender systems provide the features of personalized product and service recommendations in web based social networks by using the trust connections existing between users and preferences data available for each user. One of the main sources of user preferences data are the tags that users apply to different items. Encouraging users to apply more tags is one of the challenges faced by most social network sites. In this paper we purpose an approach to identify influential taggers in a trust based social network so that efforts to encourage tagging can be achieved by designing incentives for motivating the influential taggers to apply more tags. In our proposed approach, for every user his tagging influencer is that user in his personal network who has influenced his tagging behavior the most. We define an active user tagging actions has been influenced by a user in his personal network only when the active user tags an item after his influencer has tagged it. The influential taggers in the overall social network are those who have the influenced the maximum number of users in the network. We analyze the real life dataset of Last.fm to show that our approach is different from the current approach of defining those users who have tagged the maximum number of items as the influential users. We also discuss the implications of using our approach.