Elements of information theory
Elements of information theory
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improving collaborative filtering with trust-based metrics
Proceedings of the 2006 ACM symposium on Applied computing
Ranking robustness: a novel framework to predict query performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Trust and nuanced profile similarity in online social networks
ACM Transactions on the Web (TWEB)
What Have the Neighbours Ever Done for Us? A Collaborative Filtering Perspective
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Personalised and dynamic trust in social networks
Proceedings of the third ACM conference on Recommender systems
Ensemble methods for improving the performance of neighborhood-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Using trust in collaborative filtering recommendation
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Estimating the Query Difficulty for Information Retrieval
Estimating the Query Difficulty for Information Retrieval
Proceedings of the fourth ACM conference on Recommender systems
Adaptive active learning in recommender systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Predicting the performance of recommender systems: an information theoretic approach
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
A performance prediction approach to enhance collaborative filtering performance
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
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
The efficient imputation method for neighborhood-based collaborative filtering
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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User-based collaborative filtering systems suggest interesting items to a user relying on similar-minded people called neighbors. The selection and weighting of these neighbors characterize the different recommendation approaches. While standard strategies perform a neighbor selection based on user similarities, trust-aware recommendation algorithms rely on other aspects indicative of user trust and reliability. In this article we restate the trust-aware recommendation problem, generalizing it in terms of performance prediction techniques, whose goal is to predict the performance of an information retrieval system in response to a particular query. We investigate how to adopt the preceding generalization to define a unified framework where we conduct an objective analysis of the effectiveness (predictive power) of neighbor scoring functions. The proposed framework enables discriminating whether recommendation performance improvements are caused by the used neighbor scoring functions or by the ways these functions are used in the recommendation computation. We evaluated our approach with several state-of-the-art and novel neighbor scoring functions on three publicly available datasets. By empirically comparing four neighbor quality metrics and thirteen performance predictors, we found strong predictive power for some of the predictors with respect to certain metrics. This result was then validated by checking the final performance of recommendation strategies where predictors are used for selecting and/or weighting user neighbors. As a result, we have found that, by measuring the predictive power of neighbor performance predictors, we are able to anticipate which predictors are going to perform better in neighbor-scoring-powered versions of a user-based collaborative filtering algorithm.