Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Pointing the way: active collaborative filtering
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Collaborative Filtering Process in a Whole New Light
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Intelligent Techniques for Web Personalization: IJCAI 2003 Workshop, ITWP 2003, Acapulco, Mexico, August 11, 2003, Revised Selected Papers (Lecture Notes ... / Lecture Notes in Artificial Intelligence)
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Evaluation of recommender systems: A new approach
Expert Systems with Applications: An International Journal
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Collaborative filtering: the aim of recommender systems and the significance of user ratings
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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It is generally assumed that all users in a dataset are equally adversely affected by data sparsity and hence addressing this problem should result in improved performance. However, although all users may be members of a sparse dataset, they do not all suffer equally from the data sparsity problem. This indicates that there is some ambiguity as to which users should be identified as suffering from data sparsity, referred to as sparse users throughout this paper, and targeted with new recommendation improvement strategies. This paper defines sparsity in terms of number of item ratings and average similarity with nearest neighbours and then goes on to look at the impact of sparsity so defined on performance. Counterintuitively, it is found that in top-N recommendations sparse users actually perform better than some other categories of users when a standard approach is used. These results are explained, and empirically verified, in terms of a bias towards users with a low number of ratings. The link between sparsity and performance is also considered in the case of predictions rather than top-N recommendations. This work provides the motivation for targeting improvement approaches towards distinct groups of users as opposed to the entire dataset.