Abstract
Recommendation systems are popular both in business and in academia. A series of works have been reported. In this paper, we briefly introduce the background and some basic concepts of recommendation systems, especially the applications in mainstream websites, most of them built upon parallel processing systems. However, how the recommendation algorithm works in real applications? We investigate (1) the key ideas of recommendation algorithms that are being used in real applications and (2) the parallel architecture in those real recommendation systems. In addition, the performance of recommendation system for those sites are also being analyzed and compared. We also analyze their features and compare their performances. Finally, we outline the challenges and opportunities that all recommendation systems are facing. It is anticipated that the present review will deepen people’s understanding of the field and hence contribute to guide the future research of recommendation systems. Our work can help people to better understand the literature and guide the future directions.
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Acknowledgments
This work is supported by NSFC grants 61502161, 61472451, 61272151, the Chinese Fundamental Research Funds for the Central Universities 531107040845, and the National High-tech R&D Program of China 2014AA01A302 and 2015AA-015305.
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Li, M., Jiang, W., Li, K. (2016). Recommendation Systems in Real Applications: Algorithm and Parallel Architecture. In: Wang, G., Ray, I., Alcaraz Calero, J., Thampi, S. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2016. Lecture Notes in Computer Science(), vol 10066. Springer, Cham. https://doi.org/10.1007/978-3-319-49148-6_5
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