Abstract
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.
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Acknowledgments
This work was supported by the Ministerio de Economía y Competitividad of the Goverment of Spain under grants TIN2012-33867 and TIN2015-64282-R. The first author also wants to acknowledge the support of Ministerio de Educación, Cultura y Deporte of the Government of Spain under the grant FPU014/01724.
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Valcarce, D., Parapar, J., Barreiro, Á. (2016). Language Models for Collaborative Filtering Neighbourhoods. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_45
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DOI: https://doi.org/10.1007/978-3-319-30671-1_45
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