Automating the assignment of submitted manuscripts to reviewers
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Matrix computations (3rd ed.)
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
An algorithm for automated rating of reviewers
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Computing and using reputations for internet ratings
Proceedings of the 3rd ACM conference on Electronic Commerce
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
SIAM Journal on Discrete Mathematics
Collaborative filtering with decoupled models for preferences and ratings
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
A study of methods for normalizing user ratings in collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Scale and Translation Invariant Collaborative Filtering Systems
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Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
IEEE Transactions on Knowledge and Data Engineering
Avoiding ballot stuffing in eBay-like reputation systems
Proceedings of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems
Probabilistic models for discovering e-communities
Proceedings of the 15th international conference on World Wide Web
Mining for proposal reviewers: lessons learned at the national science foundation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Trusting advice from other buyers in e-marketplaces: the problem of unfair ratings
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Inferring binary trust relationships in Web-based social networks
ACM Transactions on Internet Technology (TOIT)
Proceedings of the 16th international conference on World Wide Web
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A formal approach to score normalization for meta-search
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Community Mining from Signed Social Networks
IEEE Transactions on Knowledge and Data Engineering
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Analyzing and Detecting Review Spam
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Controversial users demand local trust metrics: an experimental study on Epinions.com community
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A signal-to-noise approach to score normalization
Proceedings of the 18th ACM conference on Information and knowledge management
Collaborative quality filtering: establishing consensus or recovering ground truth?
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
IRFCF: iterative rating filling collaborative filtering algorithm
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Probabilistic score normalization for rank aggregation
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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The emerging trend of social information processing has resulted in Web users’ increased reliance on user-generated content contributed by others for information searching and decision making. Rating scores, a form of user-generated content contributed by reviewers in online rating systems, allow users to leverage others’ opinions in the evaluation of objects. In this article, we focus on the problem of summarizing the rating scores given to an object into an overall score that reflects the object’s quality. We observe that the existing approaches for summarizing scores largely ignores the effect of reviewers exercising different standards in assigning scores. Instead of treating all reviewers as equals, our approach models the leniency of reviewers, which refers to the tendency of a reviewer to assign higher scores than other coreviewers. Our approach is underlined by two insights: (1) The leniency of a reviewer depends not only on how the reviewer rates objects, but also on how other reviewers rate those objects and (2) The leniency of a reviewer and the quality of rated objects are mutually dependent. We develop the leniency-aware quality, or LQ model, which solves leniency and quality simultaneously. We introduce both an exact and a ranked solution to the model. Experiments on real-life and synthetic datasets show that LQ is more effective than comparable approaches. LQ is also shown to perform consistently better under different parameter settings.