Simple time-biased KNN-based recommendations
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
ACMDate
2010Citation
10.1145/1869652.1869655
CAMRa '10: Proceedings of the Workshop on Context-Aware Movie Recommendation. ACM, 2010. 20-23
ISBN
978-1-4503-0258-6DOI
10.1145/1869652.1869655Funded by
This research was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02) and the Scientific Computing Institute at UAM. The first author acknowledges support from the Chilean Government through the Becas-Chile scholarship programEditor's Version
http://dx.doi.org/10.1145/1869652.1869655Subjects
Movie recommendation; Recommender systems; Temporal Information; InformáticaNote
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CAMRa '10 Proceedings of the Workshop on Context-Aware Movie Recommendation, http://dx.doi.org/10.1145/1869652.1869655.Rights
© 2010 ACMAbstract
In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to weekly Filmtipset track. We consider simple strategies for taking into account the temporal context for movie recommendations, mainly based on variations of the KNN algorithm, which has been deeply studied in the literature, and one ad-hoc strategy, taking advantage of particular information in the weekly Filmtipset track. Results show that the usage of information near to the recommendation date alone can help improving recommendation results, with the additional benefit of reducing the information overload of the recommender engine. Furthermore, the use of social interaction information shows also a contribution in order to better predict a part of users' tastes.
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Google Scholar:Campos Soto, Pedro G.
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Bellogin Kouki, Alejandro
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Díez Rubio, Fernando
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Chavarriaga, Jaime Enrique
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