Abstract
Recommender systems are one of the most important technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative filtering CF family, have problems such as scalability and sparseness. These problems hinder further developments of recommender systems. We propose a new recommendation algorithm based on item quality and user rating preferences, which can significantly decrease the computing complexity. Besides, it is interpretable and works better when the data is sparse. Through extensive experiments on three benchmark data sets, we show that our algorithm achieves higher accuracy in rating prediction compared with the traditional approaches. Furthermore, the results also demonstrate that the problem of rating prediction depends strongly on item quality and user rating preferences, thus opens new paths for further study.
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Yuan Guan received her BS in Computer Science and Engineering from University of Electronic Science and Technology of China in 2011, and is now pursuing her MS in the Web Sciences Center, University of Electronic Science and Technology of China, China. Her research interests include data mining and recommender systems.
Shimin Cai received his BS in Electrical Engineering from Hefei University of Technology in 2004 and his PhD in Circuit and Systems from the University of Science and Technology of China in 2009. He currently serves as an associate professor of the University of Electronic Science and Technology of China. He is interested in complex network theory and its application for mining and modeling of real large-scale networked systems, time series analysis, and personalized recommendation systems.
Mingsheng Shang received his PhD in Computer Science from the University of Electronic Science and Technology of China. He is a professor of UESTC. His research interests include data mining, complex networks, and cloud computing and their applications.
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Guan, Y., Cai, S. & Shang, M. Recommendation algorithm based on item quality and user rating preferences. Front. Comput. Sci. 8, 289–297 (2014). https://doi.org/10.1007/s11704-013-3012-7
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DOI: https://doi.org/10.1007/s11704-013-3012-7