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Recommendation System for E-commerce Using Alternating Least Squares (ALS) on Apache Spark

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Intelligent Computing and Optimization (ICO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1324))

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Abstract

Recommendation system can predict the ratings of users to items by leveraging machine learning algorithms. The use of recommendation systems is common in e-commerce websites now-a-days. Since enormous amounts of data including users’ click streams, purchase history, demographics, social networking comments and user-item ratings are stored in e-commerce systems databases, the volume of the data is getting bigger at high speed, and the data is sparse. However, the recommendations and predictions must be made in real time, enabling to bring enormous benefits to human beings. Apache spark is well suited for applications which require high speed query of data, transformation and analytics results. Therefore, the recommendation system developed in this research is implemented on Apache Spark. Also, the matrix factorization using Alternating Least Squares (ALS) algorithm which is a type of collaborative filtering is used to solve overfitting issues in sparse data and increases prediction accuracy. The overfitting problem arises in the data as the user-item rating matrix is sparse. In this research a recommendation system for e-commerce using alternating least squares (ALS) matrix factorization method on Apache Spark MLlib is developed. The research shows that the RMSE value is significantly reduced using ALS matrix factorization method and the RMSE is 0.870. Consequently, it is shown that the ALS algorithm is suitable for training explicit feedback data set where users provide ratings for items.

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Author information

Authors and Affiliations

  1. BGC Trust University Bangladesh Bidyanagar, Chandanaish, Bangladesh

    Subasish Gosh, Nazmun Nahar, Mohammad Abdul Wahab & Munmun Biswas

  2. University of Chittagong, University-4331, Chittagong, Bangladesh

    Mohammad Shahadat Hossain

  3. Lulea University of Technology, 931 87, Skellefteå, Sweden

    Karl Andersson

Authors
  1. Subasish Gosh
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  2. Nazmun Nahar
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  3. Mohammad Abdul Wahab
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  4. Munmun Biswas
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  5. Mohammad Shahadat Hossain
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  6. Karl Andersson
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Corresponding author

Correspondence to Mohammad Shahadat Hossain .

Editor information

Editors and Affiliations

  1. Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Tronoh, Perak, Malaysia

    Pandian Vasant

  2. Faculty of Electrical Engineering and Computer Science, VŠB TU Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

  3. Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland

    Gerhard-Wilhelm Weber

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Gosh, S., Nahar, N., Wahab, M.A., Biswas, M., Hossain, M.S., Andersson, K. (2021). Recommendation System for E-commerce Using Alternating Least Squares (ALS) on Apache Spark. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_75

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