Improved movie recommendations based on a hybrid feature combination method

Alshammari, Gharbi, Kapetanakis, Stelios, Alshammari, Abdullah, Polatidis, Nikolaos and Petridis, Miltos (2019) Improved movie recommendations based on a hybrid feature combination method. Vietnam Journal of Computer Science, 6 (3). pp. 363-376. ISSN 2196-8888 (doi:10.1142/s2196888819500192)

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Abstract

Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing 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 method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer and Communications Engineering
Item ID: 27172
Notes on copyright: © The Author(s). This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.
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Depositing User: Jisc Publications Router
Date Deposited: 23 Jul 2019 08:54
Last Modified: 22 Aug 2019 19:45
URI: https://eprints.mdx.ac.uk/id/eprint/27172

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