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.
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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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DOI: https://doi.org/10.1007/978-3-319-91337-7_8
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