A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Efficient top-k querying over social-tagging networks
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Actively Building Private Recommender Networks for Evolving Reliable Relationships
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
Personalized social recommendations: accurate or private
Proceedings of the VLDB Endowment
Interactive itinerary planning
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Exploiting endorsement information and social influence for item recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Adaptive user profile model and collaborative filtering for personalized news
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Cubic analysis of social bookmarking for personalized recommendation
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
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With the rapid growth of information on the World Wide Web, social recommendations have appeared as one of the most important roles attracting growing attentions from researchers. Social recommendations enable a form of efficient knowledge for users and help them share contents with others. There are many studies in this area focusing on using trust-relationships in recommendation algorithm, which has become a major trend in recommendation algorithms that used for searching information precisely, feasibly and efficiently, but they neglect how to build the trust-relationships framework at start. In this work, an algorithm, called PointBurst, is proposed for building a trust-relationship framework to improve the social recommendations when there is no or too few of available trust-relationships. Here, we first construct a graphical model based on a binary-type vertex relationship, where discusses the explicit and potential connections among users and recommended items. On this basis, we implement a common-used collaborative filtering recommendation algorithm to deal with the situation of enough available trust-relationships existing, and then present PointBurst, which builds trust-relationship framework as a supplement. Finally, we crawl through data from three famous recommender websites, i.e., del.icio.us, Myspace and MovieLens and use them in experiments to show that PointBurst can suggest relevant items to users' tastes and perform better than collaborative filtering algorithm in precision and stability.