融合概率矩阵分解与ER规则的群组推荐方法

Group Recommendation Method Integrating Probability Matrix Decomposition and ER Rules

群组推荐需要同时考虑一组内所有成员的偏好,通过融合成员偏好进而向群组推荐项目。现有的对于组推荐方法的研究中大多都将相同的权重分配给群组中所有用户,而未考虑在现实生活中不同组成员的重要性和可靠性应不同。针对该问题,提出一种新的融合概率矩阵分解与证据推理(evidence reasoning,ER)规则的群组推荐方法(FPMF-ER),以改进群组推荐中个体预测和偏好融合的过程。联合用户关系信息对经典概率矩阵分解加以改进,以获取更为完整、精准的个人预测评分;在组成员偏好融合的过程中引入ER规则,根据组成员的权重和可靠性识别群组成员的影响力,使偏好融合更为合理、准确。为了验证该方法的有效性,在Book-Crossing数据集上进行了对比实验,实验结果表明,相较于最优的基准模型,FPMF-ER的推荐结果准确性和用户满意度分别至少提高了2.55%和2.06%。

Group recommendation needs to consider the preferences of all members in a group at the same time, and recommend items to the group by integrating member preferences. Most of the existing researches on group recommendation methods assign the same weight to all users in the group, without considering the importance and reliability of different group members in real life. To solve this problem, a group recommendation method(FPMF-ER)based on probabilistic matrix decomposition and evidence reasoning rules is proposed to improve the process of individual prediction and preference fusion in group recommendation. Firstly, the classical probability matrix decomposition is improved by combining user relationship information to obtain a more complete and accurate personal prediction score. Subsequently, ER rule is introduced in the process of preference fusion of group members, and the influence of group members is identified according to the weight and reliability of group members, so that preference fusion is more reasonable and accurate. In order to verify the effectiveness of the method, a comparative experiment is conducted on the Book-Crossing dataset. The experimental results show that compared with the optimal benchmark model, the accuracy of FPMF-ER recommendation results and user satisfaction are increased by at least 2.55% and 2.06%, respectively.