Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
IEEE Transactions on Knowledge and Data Engineering
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Private distributed collaborative filtering using estimated concordance measures
Proceedings of the 2007 ACM conference on Recommender systems
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
The effect of correlation coefficients on communities of recommenders
Proceedings of the 2008 ACM symposium on Applied computing
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Improving Prediction Accuracy in Trust-Aware Recommender Systems
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
How far are we in trust-aware recommendation?
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Hybrid user preference models for second life and opensimulator virtual worlds
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
A simple but effective method to incorporate trusted neighbors in recommender systems
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Using graph partitioning techniques for neighbour selection in user-based collaborative filtering
Proceedings of the sixth ACM conference on Recommender systems
Prior ratings: a new information source for recommender systems in e-commerce
Proceedings of the 7th ACM conference on Recommender systems
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Our research aims to tackle the problems of data sparsity and cold start of traditional recommender systems. Insufficient ratings often result in poor quality of recommendations in terms of accuracy and coverage. To address these issues, we propose three different approaches from the perspective of preference modelling. Firstly, we propose to merge the ratings of trusted neighbors and thus form a new rating profile for the active users, based on which better recommendations can be generated. Secondly, we aim to make better use of user ratings and introduce a novel Bayesian similarity measure by taking into account both the direction and length of rating vectors. Thirdly, we propose a new information source called prior ratings based on virtual product experience in virtual reality environments, in order to inherently resolve the concerned problems.