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Correction published on 4 August 2016, see Sensors 2016, 16(8), 1230.

Open AccessArticle
Sensors 2016, 16(2), 143;

A Context-Aware Mobile User Behavior-Based Neighbor Finding Approach for Preference Profile Construction

School of Information, Qilu University of Technology, #3501 Daxue Road, Changqing District, Jinan 250353, China
School of Informatics, Linyi University, Shuangling Rd., Lanshan, Linyi 276005, China
This paper is an extended version of our paper published in Procedia Computer Science. Gao, Q.; Dong, X.; Fu, D. A Context-Aware Mobile User Behavior Based Preference Neighbor Finding Approach for Personalized Information Retrieval. In the Proceedings of the CHARMS 2015 Workshop, Belfort, France, 17–20 August 2015; Volume 56C, pp. 471–476.
Author to whom correspondence should be addressed.
Academic Editors: Eric T. Matson, Byung-Cheol Min and Donghan Kim
Received: 31 October 2015 / Revised: 31 December 2015 / Accepted: 18 January 2016 / Published: 23 January 2016
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In this paper, a new approach is adopted to update the user preference profile by seeking users with similar interests based on the context obtainable for a mobile network instead of from desktop networks. The trust degree between mobile users is calculated by analyzing their behavior based on the context, and then the approximate neighbors are chosen by combining the similarity of the mobile user preference and the trust degree. The approach first considers the communication behaviors between mobile users, the mobile network services they use as well as the corresponding context information. Then a similarity degree of the preference between users is calculated with the evaluation score of a certain mobile web service provided by a mobile user. Finally, based on the time attenuation function, the users with similar preference are found, through which we can dynamically update the target user’s preference profile. Experiments are then conducted to test the effect of the context on the credibility among mobile users, the effect of time decay factors and trust degree thresholds. Simulation shows that the proposed approach outperforms two other methods in terms of Recall Ratio, Precision Ratio and Mean Absolute Error, because neither of them consider the context mobile information. View Full-Text
Keywords: multi-agent; context; trust degree; interest similarity degree; time attenuation multi-agent; context; trust degree; interest similarity degree; time attenuation

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Gao, Q.; Fu, D.; Dong, X. A Context-Aware Mobile User Behavior-Based Neighbor Finding Approach for Preference Profile Construction. Sensors 2016, 16, 143.

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