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Mixed-Profiling Recommender Systems for Big Data Environment

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Lecture Notes in Real-Time Intelligent Systems (RTIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 756))

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Abstract

Recommender systems are intelligent tools that analyze the overall users’ interests and tastes and provide them with appropriate recommendations. Such systems face several challenges in a Big Data environment due to the growing size of the recommendation matrix and the huge number of the missing values it contains. Moreover, the inaccuracy of the ratings provided by some users has a negative impact on system’s performances. In this paper, we present a Big Data mixed-profiling approach that aims to reduce matrix sparsity by integrating explicit and implicit feedbacks and improve the relevance of recommendations by using a profile for each user that reflects his level of reliability and expertise. Our approach is validated in the context of digital library recommender system using Apache Spark processing engine.

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Acknowledgments

The authors gratefully thank Mr. Mohammed Amaadid, Mr. Abdelkrim Tahiri and Mr. Amine Sennouni, graduate students from the School of Information Sciences in Rabat for their cooperation and invaluable contribution to the validation of this work.

Author information

Authors and Affiliations

  1. SIP Research Team, Rabat IT Center, EMI, Mohammed V University, Rabat, Morocco

    Siham Yousfi & Dalila Chiadmi

  2. IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco

    Maryem Rhanoui

  3. Meridian Team, LYRICA Laboratory, School of Information Sciences, Rabat, Morocco

    Siham Yousfi & Maryem Rhanoui

Authors
  1. Siham Yousfi
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  2. Maryem Rhanoui
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  3. Dalila Chiadmi
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Corresponding author

Correspondence to Siham Yousfi .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Institute of Mathematics and Computer Science, Opole University, Opole, Poland

    Jolanta Mizera-Pietraszko

  2. Digital Information Research Foundation , Chennai, India

    Pit Pichappan

  3. University of Hassan II , Casablanca, Morocco

    Lahby Mohamed

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Yousfi, S., Rhanoui, M., Chiadmi, D. (2019). Mixed-Profiling Recommender Systems for Big Data Environment. In: Mizera-Pietraszko, J., Pichappan, P., Mohamed, L. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2017. Advances in Intelligent Systems and Computing, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-91337-7_8

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