A Social TopN Recommendation Scheme Based on Grey Forecast Model.

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Bibliographic Details
Title:	A Social TopN Recommendation Scheme Based on Grey Forecast Model.
Authors:	Yunpeng Xiao¹ xiaoyp@cqupt.edu.cn, Keyi Zhang¹, Ming Xu², Yanbing Liu¹
Source:	Journal of Grey System. 2018, Vol. 30 Issue 4, p78-96. 19p.
Subjects:	Gray forecasting model, Social network theory, Discretization methods, Algorithms, Standard deviations
Abstract:	In view of data nonuniformity and sparseness existing in recommendation schemes, this study introduces and optimizes grey system theory model in application scenarios of social network. Furthermore, a new topN recommendation scheme is proposed. Firstly, by analyzing the rating behavior of users, the factors that affect rating are discovered. To quantify the factors, time discretization method is leveraged as well as three aspects in recommendation research: user, item and context. Secondly, in regard to the problem of time nonuniformity in observed sequences, this paper changes the non-uniform sequences into equal interval, which optimizes the GM(1,N) model of grey system and expands its application scope. Finally, considering the timeliness of user preference, a time decay function is introduced for the equal interval sequences to optimize the grey forecast model and reduce its error rate in prediction. Besides, the improved grey forecast model is applied to mine the explicit relationship between user rating and relatedfactors, and construct rating prediction algorithm to improve the recommendation accuracy on the interest list of target users. The experimental results reveal the efficiency of our algorithm both in mean absolute error (MAE) and root mean square error (RMSE). Moreover, the proposed recommendation scheme has favorable precision, recall and F-measure. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	In view of data nonuniformity and sparseness existing in recommendation schemes, this study introduces and optimizes grey system theory model in application scenarios of social network. Furthermore, a new topN recommendation scheme is proposed. Firstly, by analyzing the rating behavior of users, the factors that affect rating are discovered. To quantify the factors, time discretization method is leveraged as well as three aspects in recommendation research: user, item and context. Secondly, in regard to the problem of time nonuniformity in observed sequences, this paper changes the non-uniform sequences into equal interval, which optimizes the GM(1,N) model of grey system and expands its application scope. Finally, considering the timeliness of user preference, a time decay function is introduced for the equal interval sequences to optimize the grey forecast model and reduce its error rate in prediction. Besides, the improved grey forecast model is applied to mine the explicit relationship between user rating and relatedfactors, and construct rating prediction algorithm to improve the recommendation accuracy on the interest list of target users. The experimental results reveal the efficiency of our algorithm both in mean absolute error (MAE) and root mean square error (RMSE). Moreover, the proposed recommendation scheme has favorable precision, recall and F-measure. [ABSTRACT FROM AUTHOR]
ISSN:	09573720