Abstract
Recommender systems are tools to help users find items that they deem of interest to them. They can be seen as an application of data mining process. In this paper, a new recommender system based on multi-features is introduced. Demographic and psychographic features are used to asses similarities between users. The model is built on a collaborative filtering method and addresses three problems: sparsity, scalability and cold-star. The sparsity problem is tackled by integrating users-documents relevant information within meta-clusters. The scalability and the cold-start problems are considered by using a suitable probability model calculated on meta-cluster information. Moreover, a weight similarity measure is introduced in order to take into account dynamic human being preferences behaviour. A prediction score for generating recommendations is proposed based on the target user previous behaviour and his/her neighbourhood preferences on the target document.
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Trujillo, M., Millan, M., Ortiz, E. (2007). A Recommender System Based on Multi-features. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74477-1_35
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DOI: https://doi.org/10.1007/978-3-540-74477-1_35
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