Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Information Retrieval
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Convex Optimization
A study of mixture models for collaborative filtering
Information Retrieval
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
Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic relevance ranking for collaborative filtering
Information Retrieval
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
Adaptive collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Long Tail Recommender Utilizing Information Diffusion Theory
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Mean-Variance Analysis: A New Document Ranking Theory in Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Enhancing diversity in Top-N recommendation
Proceedings of the third ACM conference on Recommender systems
Reducing the risk of query expansion via robust constrained optimization
Proceedings of the 18th ACM conference on Information and knowledge management
Goal-driven collaborative filtering – a directional error based approach
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Click shaping to optimize multiple objectives
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Precision-oriented evaluation of recommender systems: an algorithmic comparison
Proceedings of the fifth ACM conference on Recommender systems
Stochastic matching and collaborative filtering to recommend people to people
Proceedings of the fifth ACM conference on Recommender systems
Auralist: introducing serendipity into music recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
Using control theory for stable and efficient recommender systems
Proceedings of the 21st international conference on World Wide Web
Personalized click shaping through lagrangian duality for online recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Multiple objective optimization in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
A 3D approach to recommender system evaluation
Proceedings of the 2013 conference on Computer supported cooperative work companion
Content recommendation on web portals
Communications of the ACM
Facing the cold start problem in recommender systems
Expert Systems with Applications: An International Journal
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This paper is about the utility of making personalized recommendations. While it is important to accurately predict the target user's preference, in practice the accuracy should not be the only concern; a useful recommender system needs to consider the user's utility or satisfaction of fulfilling a certain information seeking task. For example, recommending popular items (products) is unlikely to result in more gain than discovering insignificant ("long tail") yet liked items because the popular ones might be already known to the user. Equally, recommending items that are out of stock would be frustrating for both the user and system if the system is employed to discover items to purchase. Thus, it is important to have a flexible recommendation framework that takes into account additional recommendation goals meanwhile minimizing the performance loss in order to provide greater adjustability and a better user experience. To achieve this, in this paper, we propose a general recommendation optimization framework that not only considers the predicted preference scores (e.g. ratings) but also deals with additional operational or resource related recommendation goals. Using this framework we demonstrate through realistic examples how to expand existing rating prediction algorithms by biasing the recommendation depending on other external factors such as the availability, profitability or usefulness of an item. Our experiments on real data sets demonstrate that this framework is indeed able to cope with multiple objectives with minor performance loss.