| 基于偏好融合的群组推荐研究 |
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| 引用本文： | 汪祥舜,郑孝遥,朱德义,章玥,孙丽萍.基于偏好融合的群组推荐研究[J].南京气象学院学报,2019,11(5):601-608. |
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| 作者姓名： | 汪祥舜 郑孝遥 朱德义 章玥 孙丽萍 |
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| 作者单位： | 安徽师范大学 计算机与信息学院, 芜湖, 241002;网络与信息安全安徽省重点实验室(安徽师范大学), 芜湖, 241002,安徽师范大学 计算机与信息学院, 芜湖, 241002;网络与信息安全安徽省重点实验室(安徽师范大学), 芜湖, 241002,安徽师范大学 计算机与信息学院, 芜湖, 241002;网络与信息安全安徽省重点实验室(安徽师范大学), 芜湖, 241002,华东师范大学 上海市高可信计算重点实验室, 上海, 200062,安徽师范大学 计算机与信息学院, 芜湖, 241002;网络与信息安全安徽省重点实验室(安徽师范大学), 芜湖, 241002 |
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| 基金项目： | 国家自然科学基金（61772034，61602009）；安徽省自然科学基金（1808085MF172，1908085MF190）；高校优秀青年人才支持计划重点项目（gxyqZD2019010）；国家重点研发计划重点专项（2018YFB2101301）；上海市高可信计算重点实验室开放课题（07dz22304201607） |
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| 摘 要： | 传统的推荐系统主要针对单个用户，但随着社会和电子商务的快速发展，人们越来越多地以多个用户的形式一起参与活动，而群组推荐旨在为多个用户组成的群组提供服务，已成为当前研究的热点之一.针对目前群组推荐准确率低，群组成员之间偏好冲突难以融合的问题，本文提出了一种新的共识模型策略，融合了群组领袖影响因子和项目热度影响因子，基于K近邻为目标群组寻找邻居群组，借鉴邻居群组的偏好，设计了基于偏好融合的群组推荐算法.在MovieLens数据集上的实验结果表明，本文所提的融合策略较传统的偏好融合策略有着更优越的表现，推荐准确率（nDCG）的总体平均性能约提高13%，推荐列表多样性指标的总体平均性能约提高10%.
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| 关 键 词： | 群组推荐 推荐系统 偏好融合 协同过滤 数据挖掘 偏好预测 |
| 收稿时间： | 2019/9/7 0:00:00 |
Research on group recommendation based on preference aggregation |
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| WANG Xiangshun,ZHENG Xiaoyao,ZHU Deyi,ZHANG Yue and SUN Liping.Research on group recommendation based on preference aggregation[J].Journal of Nanjing Institute of Meteorology,2019,11(5):601-608. |
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| Authors: | WANG Xiangshun ZHENG Xiaoyao ZHU Deyi ZHANG Yue and SUN Liping |
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| Institution: | School of Computer and Information, Anhui Normal University, Wuhu 241002;Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu 241002,School of Computer and Information, Anhui Normal University, Wuhu 241002;Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu 241002,School of Computer and Information, Anhui Normal University, Wuhu 241002;Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu 241002,Shanghai Key Lab for Trustworthy Computing, East China Normal University, Shanghai 200062 and School of Computer and Information, Anhui Normal University, Wuhu 241002;Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu 241002 |
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| Abstract: | The traditional recommendation system is mainly for a single user,but people are increasingly participating in activities in the form of multiple users.Group recommendation is intended to serve groups of multiple users and has already become one of the hotspots of current research.Aiming at the low accuracy of current group recommendation and the difficulty of integrating preference conflicts among group members,this paper proposes a new consensus model strategy,which combines group leader impact factor and project heat impact factor,and is based on K Nearest Neighbor recommendation.A group recommendation algorithm based on preference aggregation is designed to find the neighbor groups for the target group and draw on the preference of the neighbor groups.The experimental results on the MovieLens dataset show that the aggregation strategy proposed in this paper has better performance than traditional preference aggregation strategy.The overall average performance of the recommended accuracy (measured by normalized Discounted Cumulative Gain,nDCG) is increased by about 13%,and the overall average performance of the recommendation list diversity is improved by about 10%. |
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| Keywords: | group recommendation recommendation preference aggregation collaborative filtering data mining preference prediction |
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