Language Models are state-of-the-art methods in Information Retrieval. Their sound statistical foundation and high effectiveness in several retrieval tasks are key to their current success. In this paper, we explore how to apply these models to deal with the task of computing user or item neighbourhoods in a collaborative filtering scenario. Our experiments showed that this approach is superior to other neighbourhood strategies and also very efficient. Our proposal, in conjunction with a simple neighbourhood-based recommender, showed a great performance compared to state-of-the-art methods (NNCosNgbr and PureSVD) while its computational complexity is low.
CITATION STYLE
Valcarce, D., Parapar, J., & Barreiro, Á. (2016). Language models for collaborative filtering Neighbourhoods. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9626, pp. 614–625). Springer Verlag. https://doi.org/10.1007/978-3-319-30671-1_45
Mendeley helps you to discover research relevant for your work.