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| Study of a Group Recommendation Model of Integrating Context Information in a Mobile Environment—Empirical Analysis Based on User APP Behavior Data |
| Xia Lixin, Yang Jinqing, Cheng Xiufeng |
| School of Information Management, Central China Normal University, Wuhan 430079 |
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Abstract In order to improve the accuracy of recommended results in the group recommendation model, a group recommendation model integrating context information is proposed in this paper. Firstly, the user behavior context data are obtained, and the preference represented by individual user behavior is extracted. Secondly, the behavior similarity of individual users is calculated and cluster discovery is conducted. Subsequently, the group behavior characteristics are mined from context data, and then a feature vector of group behavioral preference is built. Finally, collaborative recommendation ideas are combined for the group as a whole. The collaboration also occurs with other groups producing an item history score to form a prediction score. In the experiment, we analyze the user’s operation flow, extract the theme sequence features, and then incorporate the classic context information to produce the recommendation results. The results show that the top-6 of the recommended results obtained by using this model are more accurate than those recommended by traditional (non-situational) groups. Therefore, this model is more suitable for group recommendations in mobile environments.
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Received: 14 November 2017
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