GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A music recommendation system based on music and user grouping
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
Novel Item Recommendation by User Profile Partitioning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Recommender Systems Handbook
Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation
ACM Transactions on Internet Technology (TOIT)
Workshop on novelty and diversity in recommender systems - DiveRS 2011
Proceedings of the fifth ACM conference on Recommender systems
Diversification and refinement in collaborative filtering recommender
Proceedings of the 20th ACM international conference on Information and knowledge management
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Knowledge-Based Systems
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
A multi-agent recommender system for supporting device adaptivity in e-Commerce
Journal of Intelligent Information Systems
An implementation and evaluation of recommender systems for traveling abroad
Expert Systems with Applications: An International Journal
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The major aim of recommender algorithms has been to predict accurately the rating value of items. However, it has been recognized that accurate prediction of rating values is not the only requirement for achieving user satisfaction. One other requirement, which has gained importance recently, is the diversity of recommendation lists. Being able to recommend a diverse set of items is important for user satisfaction since it gives the user a richer set of items to choose from and increases the chance of discovering new items. In this study, we propose a novel method which can be used to give each user an option to adjust the diversity levels of their own recommendation lists. Experiments show that the method effectively increases the diversity levels of recommendation lists with little decrease in accuracy. Compared to the existing methods, the proposed method, while achieving similar diversification performance, has a very low computational time complexity, which makes it highly scalable and allows it to be used in the online phase of the recommendation process.