Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Studying Recommendation Algorithms by Graph Analysis
Journal of Intelligent Information Systems
Content-Independent Task-Focused Recommendation
IEEE Internet Computing
ACM Transactions on Information Systems (TOIS)
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Research Paper Recommender Systems: A Random-Walk Based Approach
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
IEEE Transactions on Knowledge and Data Engineering
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The task-dependent effect of tags and ratings on social media access
ACM Transactions on Information Systems (TOIS)
A modified random walk framework for handling negative ratings and generating explanations
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Bridging memory-based collaborative filtering and text retrieval
Information Retrieval
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Social media allow users to give their opinion about the available content by assigning a rating. Collaborative filtering approaches to predict recommendations based on these graded relevance assessments are hampered by the sparseness of the data. This sparseness problem can be overcome with graph-based models, but current methods are not able to deal with negative relevance assessments. We propose a new graph-based model that exploits both positive and negative preference data. Hereto, we combine in a single content ranking the results from two graphs, one based on positive and the other based on negative preference information. The resulting ranking contains less false positives than a ranking based on positive information alone. Low ratings however appear to have a predictive value for relevant content. Discounting the negative information therefore does not only remove the irrelevant content from the top of the ranking, but also reduces the recall of relevant documents.