GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Precision-oriented evaluation of recommender systems: an algorithmic comparison
Proceedings of the fifth ACM conference on Recommender systems
User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm
Proceedings of the 2013 conference on Computer supported cooperative work
Relevance-based language modelling for recommender systems
Information Processing and Management: an International Journal
Probabilistic collaborative filtering with negative cross entropy
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM conference on Recommender systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Spectral clustering techniques have become one of the most popular clustering algorithms, mainly because of their simplicity and effectiveness. In this work, we make use of one of these techniques, Normalised Cut, in order to derive a cluster-based collaborative filtering algorithm which outperforms other standard techniques in the state-of-the-art in terms of ranking precision. We frame this technique as a method for neighbour selection, and we show its effectiveness when compared with other cluster-based methods. Furthermore, the performance of our method could be improved if standard similarity metrics -- such as Pearson's correlation -- are also used when predicting the user's preferences.