An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Proceedings of the 10th international conference on Intelligent user interfaces
Explanation in Recommender Systems
Artificial Intelligence Review
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
The effects of transparency on trust in and acceptance of a content-based art recommender
User Modeling and User-Adapted Interaction
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Stacking recommendation engines with additional meta-features
Proceedings of the third ACM conference on Recommender systems
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
MoviExplain: a recommender system with explanations
Proceedings of the third ACM conference on Recommender systems
Hybrid web recommender systems
The adaptive web
ACM Transactions on the Web (TWEB)
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Recommender systems are intended to assist consumers by making choices from a large scope of items. While most recommender research focuses on improving the accuracy of recommender algorithms, this paper stresses the role of explanations for recommended items for gaining acceptance and trust. Specifically, we present a method which is capable of providing detailed explanations of recommendations while exhibiting reasonable prediction accuracy. The method models the users' ratings as a function of their utility part-worths for those item attributes which influence the users' evaluation behavior, with part-worth being estimated through a set of auxiliary regressions and constrained optimization of their results. We provide evidence that under certain conditions the proposed method is superior to established recommender approaches not only regarding its ability to provide detailed explanations but also in terms of prediction accuracy. We further show that a hybrid recommendation algorithm can rely on the content-based component for a majority of the users, switching to collaborative recommendation only for about one third of the user base.