A Hybrid Feature Combination Method that Improves Recommendations

Gharbi Alshammari, Stylianos Kapetanakis, Abduallah Alshammari, Nikolaos Polatidis, Miltos Petridis

    Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


    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.
    Original languageEnglish
    Title of host publicationInternational Conference on Computational Collective Intelligence
    Number of pages10
    ISBN (Print)9783319984421
    Publication statusPublished - 8 Aug 2018
    EventInternational Conference on Computational Collective Intelligence - Bristol, United Kingdom
    Duration: 5 Sept 20187 Sept 2018
    Conference number: 10

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743


    ConferenceInternational Conference on Computational Collective Intelligence
    Abbreviated titleICCCI
    Country/TerritoryUnited Kingdom
    Internet address


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