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A Recommendation System for Movies by Using Hadoop Mapreduce

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Advanced Technologies, Systems, and Applications VIII (IAT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 644))

  • 345 Accesses

Abstract

Recommendation systems have become an integral component of the sales strategies of many businesses. Due to the immense size of data sets, however, innovative algorithms such as collaborative filtering, clustering models, and search-based methods are utilized. This study intends to demonstrate the benefits of the Hadoop MapReduce framework and item-to-item collaborative filtering by developing a user-ratings-based recommendation system for a larger movie data set. The resulting system offers information on movies filtered by year, director name, or comparable movies based on user reviews. Thus, we have been able to deliver credible movie suggestions based on these lists. The evaluation indicates that the recommended approaches are accurate and reliable.

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Author information

Authors and Affiliations

  1. International Burch University, Sarajevo, Bosnia and Herzegovina

    Dinko Omeragić, Aldin Beriša & Samed Jukić

  2. University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Dino Kečo & Bećir Isaković

Authors
  1. Dinko Omeragić
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  2. Aldin Beriša
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  3. Dino Kečo
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  4. Samed Jukić
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  5. Bećir Isaković
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Corresponding author

Correspondence to Dinko Omeragić .

Editor information

Editors and Affiliations

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

    Naida Ademović

  2. International Burch University, Francuske revolucije bb, Ilidža, Bosnia and Herzegovina

    Jasmin Kevrić

  3. University of Utah, Salt Lake City, UT, USA

    Zlatan Akšamija

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Omeragić, D., Beriša, A., Kečo, D., Jukić, S., Isaković, B. (2023). A Recommendation System for Movies by Using Hadoop Mapreduce. In: Ademović, N., Kevrić, J., Akšamija, Z. (eds) Advanced Technologies, Systems, and Applications VIII. IAT 2023. Lecture Notes in Networks and Systems, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-031-43056-5_24

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