Abstract
Recommender systems help users find relevant items efficiently based on their interests and historical interactions. They can also be beneficial to businesses by promoting the sale of products. Recommender systems can be modelled by applying different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we propose a novel hybrid approach that combines user-user CF with the attributes of DF to indicate the nearest users, and compare the Random Forest classifier against the kNN classifier, developed through an investigation of ways to reduce the errors in rating predictions based on users past interactions. Our combined method leads to improved prediction accuracy in two different classification algorithms. The main goal of this paper is to identify the impact of DF on CF and compare the two classifiers. We apply a feature combination hybrid method that can improve prediction accuracy and achieve lower mean absolute error values compared with the results of CF or DF alone. To test our approach, we ran an offline evaluation using the 1 M MovieLens data set.
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Alshammari, G., Kapetanakis, S., Alshammari, A., Polatidis, N., Petridis, M. (2018). A Hybrid Feature Combination Method that Improves Recommendations. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_19
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