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Reliable Book Recommender System: An Evaluation and Comparison of Collaborative Filtering Algorithms

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Advanced Technologies, Systems, and Applications VI (IAT 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 316))

Abstract

Nowadays, a vast number of online businesses and services use recommender systems to provide relevant product suggestions to their customers. Recommender systems can be defined as systems employing various data mining techniques to predict users’ interest in certain items or topics, among a tremendous amount of available data. While there exist varying methodologies to construct a recommender system, one of the most potent and commonly used techniques is collaborative filtering (CF), which evaluates items based on the opinions of other users. This paper examines several commonly used collaborative filtering models with the goal of creating a reliable book recommendation system. The research focuses on training, evaluating and comparing several neighborhood-based and matrix factorization-based recommender algorithms, using a combination of two book datasets: the BookCrossing dataset compiled in 2004, and the Goodbooks “most popular books” dataset from 2017. Grid search with cross-validation is used to find the best performing combination of several hyper-parameters and different CF models. Ultimately, a matrix factorization-based SVD++ approach is selected as the most performant model, with MAE of 0.57 and RMSE of 0.752, making it a considerably good model based on the provided data. Moreover, a manual comparison between a user’s book history and their recommended book titles shows that users are generally recommended books of the same or similar genres. Lastly, the paper discusses certain shortcomings of the proposed model and offers several suggestions for future improvements.

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Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Department of Information Technologies, International Burch University, Sarajevo, Bosnia and Herzegovina

    Aldin Kovačević & Zerina Mašetić

Authors
  1. Aldin Kovačević
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  2. Zerina Mašetić
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Corresponding author

Correspondence to Aldin Kovačević .

Editor information

Editors and Affiliations

  1. Faculty of Civil Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Naida Ademović

  2. Faculty of Technical Engineering, University of Bihać, Bihać, Bosnia and Herzegovina

    Edin Mujčić

  3. University of Massachusetts Amherst, Hadley,, MA, USA

    Zlatan Akšamija

  4. International Burch University, Ilidža, Bosnia and Herzegovina

    Jasmin Kevrić

  5. Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Samir Avdaković

  6. Department of Mathematics, Wellesley College, Wellesley Hills, MA, USA

    Ismar Volić

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Kovačević, A., Mašetić, Z. (2022). Reliable Book Recommender System: An Evaluation and Comparison of Collaborative Filtering Algorithms. In: Ademović, N., Mujčić, E., Akšamija, Z., Kevrić, J., Avdaković, S., Volić, I. (eds) Advanced Technologies, Systems, and Applications VI. IAT 2021. Lecture Notes in Networks and Systems, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-030-90055-7_20

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