Computational Methods for Intelligent Information Access
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Modern Information Retrieval
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
Analysis of Methods for Novel Case Selection
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Enhancing diversity in Top-N recommendation
Proceedings of the third ACM conference on Recommender systems
Improving tag-based recommendation by topic diversification
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Cluster searching strategies for collaborative recommendation systems
Information Processing and Management: an International Journal
Exploiting the diversity of user preferences for recommendation
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
Clustering-based diversity improvement in top-N recommendation
Journal of Intelligent Information Systems
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Standard top-N collaborative recommendation algorithms are very poor at recommending relevant products to a user that are more novel than her average tastes. Our study shows that novel recommendation is difficult because standard similarity metrics measure the aggregate similarity to multiple items in the user profile and the influence of more novel items is lost in the aggregation. To better capture the user's range of tastes, we propose to partition the user profile into clusters of similar items and compose the recommendation list of items that match well with each cluster, rather than with the entire user profile. In this paper we evaluate a number of partitioning strategies in combination with a dimension reduction strategy. A new evaluation methodology is introduced to capture the system ability to diversify its recommendations across relevant items regardless of their novelty. By plotting concentration curves of novelty against accuracy, we show that this strategy succeeds in reducing the system bias towards similar items at a small cost to overall accuracy.