An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Knowledge and Information Systems
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Feature-Weighted User Model for Recommender Systems
UM '07 Proceedings of the 11th international conference on User Modeling
Improving top-n recommendation techniques using rating variance
Proceedings of the 2008 ACM conference on Recommender systems
Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
KMulE: a framework for user-based comparison of recommender algorithms
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm
Proceedings of the 2013 conference on Computer supported cooperative work
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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Collaborative filtering recommender systems provide their users with relevant items based on information from other similar users. Popular collaborative filtering approaches such as Pearson correlation coefficient and cosine similarity, compute the similarity between users based on the set of their co-rated items. However, similarities are commonly computed without taking the popularity of the set of two users' co-rated items into consideration, e.g. an item rated by very many users should have less impact on the similarity measure, and analogously an item rated by few should have a larger impact on the similarity score of two users. In this paper, we investigate the effects of common weighting schemes on different types of users, i.e. new users with few ratings (so-called cold start users), post cold start users, and power users. Empirical studies over two datasets have shown in which of these cases weighting schemes are beneficial in terms of recommendation quality.