GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Experiments in Sparsity Reduction: Using Clustering in Collaborative Recommenders
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Recommender systems using linear classifiers
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A Dynamic Framework for Maintaining Customer Profiles in E-Commerce Recommender Systems
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
On the enhancement of collaborative filtering by demographic data
Web Intelligence and Agent Systems
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A Multi-clustering Hybrid Recommender System
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
A multilayer ontology-based hybrid recommendation model
AI Communications - Recommender Systems
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A Scalable, Accurate Hybrid Recommender System
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A scalable tag-based recommender system for new users of the social web
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Incremental Kernel Mapping Algorithms for Scalable Recommender Systems
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Kernel-Mapping Recommender system algorithms
Information Sciences: an International Journal
A hybrid recommendation approach for a tourism system
Expert Systems with Applications: An International Journal
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
| Hi-index | 12.05 |
Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance - accuracy and coverage - of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.