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A Triangle Multi-level Item-Based Collaborative Filtering Method that Improves Recommendations

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Engineering Applications of Neural Networks (EANN 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 893))

Abstract

One of the most successful approaches that can provide a relevant recommendation in various domains is collaborative filtering. Although this approach has been widely applied, there are still limitations to be overcome in this research area. Accuracy is still one of the areas that need to be improved. In addition, the rapid growth of information available online presents recommender systems with several challenges. More specifically, data sparsity and coverage affect the quality of the recommendations that can be provided. In this paper, we propose an item-based collaborative filtering (IBCF) approach with triangle similarity measures that take into account the length and angle of rating vectors between users and allow positive and negative adjustments using a multi-level recommendation approach. We have improved the predictive accuracy and effectiveness of the proposed method, which outperforms all the compared methods in terms of the mean absolute error (MAE) and the root mean squared error (RMSE). We aimed to evaluate the proposed method by comparing our results with those of some popular similarity measures using k-nearest neighbour (kNN) algorithms. We ran our experiment using three real dataset: MovieLens 100K, MovieLens 1M and Yahoo! Movies.

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Authors and Affiliations

  1. School of Computing, Engineering and Mathematics, University of Brighton, Moulsecoomb Campus, Lewes Road, Brighton, BN2 4GJ, UK

    Gharbi Alshammari, Stelios Kapetanakis & Nikolaos Polatidis

  2. Department of Computer Science, Middlesex University London, The Burroughs, London, NW4 4BT, UK

    Miltos Petridis

  3. Gluru Research, 71-91 Aldwych, London, WC2B 4HN, UK

    Stelios Kapetanakis

Authors
  1. Gharbi Alshammari
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  2. Stelios Kapetanakis
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  3. Nikolaos Polatidis
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  4. Miltos Petridis
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Corresponding author

Correspondence to Gharbi Alshammari .

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, United Kingdom

    Elias Pimenidis

  2. Oxford Brookes University, Oxford, United Kingdom

    Chrisina Jayne

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Alshammari, G., Kapetanakis, S., Polatidis, N., Petridis, M. (2018). A Triangle Multi-level Item-Based Collaborative Filtering Method that Improves Recommendations. In: Pimenidis, E., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2018. Communications in Computer and Information Science, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-319-98204-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-98204-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98203-8

  • Online ISBN: 978-3-319-98204-5

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