移动环境下融合情境信息的群组推荐模型研究——基于用户APP行为数据的实证分析

Study of a Group Recommendation Model of Integrating Context Information in a Mobile Environment——Empirical Analysis Based on User APP Behavior Data

为了提高群组推荐模型中推荐结果的准确度问题,本文研究并提出了一种融合情境信息的群组推荐模型。首先,获取用户行为情境数据,同时发掘提取单个用户行为的偏好;其次,计算单个用户行为相似度,进行群组聚类发现;然后,融入情境信息挖掘群组行为特征,并构建群组行为偏好特征向量,最后结合协同推荐思想,将群组作为整体,和其他群组对项目的历史评分进行协同,形成预测评分。在实验中,我们通过分析用户的操作流,提取了主题序列特征,然后融入了经典情境信息,得出推荐结果。结果表明,使用该模型得出的排序靠前(6位)的推荐结果较之传统(非情境)的群组推荐方法具有更高的准确性。因此,该模型更适用于移动环境下的群组推荐。

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.