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Recommendation algorithm based on item quality and user rating preferences |
Yuan GUAN,Shimin CAI,Mingsheng SHANG( ) |
Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China |
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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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Keywords
recommendation algorithm
item quality
user rating preferences
RMSE
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Corresponding Author(s):
Mingsheng SHANG
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Issue Date: 24 June 2014
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