群组推荐分析与研究综述

doi:10.11959/j.issn.1000-0801.2018306

Abstract

Abstract:

With the advent of age of big data,the application fields of recommendation systems have become increasingly widespread.The recommendation service object of the group recommendation systems are expanded from a single user to a group of members,and becoming one of the research hotspots in the recommendation system fields.The group recommendation system needs to consider the preferences of all group members,and to fuse the preferences of the members,and to alleviate the conflicts of preferences among them,so that the recommendation results satisfy all group members as much as possible.The recent research progress of the group recommendation was mainly reviewed.The frontiers of group classification,group detection and group prediction recommendation were summarized.And main factors of the group recommendations were generalized.Finally,the research points of group recommendation and its prospects were separately elaborated.

Key words: group recommendation, group detection, preference aggregation, group modeling, recommendation system

CLC Number:

TP391

Yunchang WU,Baisong LIU,Yangyang WANG,Chenjie FEI. Review of group recommendation analysis and research[J]. Telecommunications Science, 2018, 34(12): 71-83.

Figures/Tables 5

References 86

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策略	优点	缺点
加法/连乘策略	执行简单	可能将多个成员评价的低分项目预测为偏好项目而忽略少数成员评价的高分项目
均值策略	一定的隐私保护性能	推荐的项目评分可能具有高偏差
最小痛苦/最开心/最受尊敬者策略	仅考虑单独用户，执行简单	单一成员来确定群体偏好有失偏颇，难以把握群体的整体偏好。其中最小痛苦策略易受恶意评分影响
多元投票/赞成投票策略	考虑大多数用户偏好，有一定公平性	未考虑负面偏好，其中多元投票中分拣耗时较长
Borda计数/公平策略	充分考虑项目优先级	排名优先高度依赖高平均分数和数量且没有考虑偏差
平均避免痛苦	避免恶性评分且具有一定公平性	仅排除极端情况，没有考虑群体成员的整体偏差

群组推荐技术	特点	不足
基于深度学习的组推荐	捕捉群组成员与项目之间的非线性关系，在不同群组甚至不同项目上动态分配权值，实现群组的动态推荐	可解释性不强
基于智能算法的组推荐	结合算法自身的优越性，根据成员间相似关系和相互作用等影响，量化成员在群组中的权值	同时也带来智能算法自身的缺陷；需要群组和用户的共同评分项；实时性相对深度学习较差
基于符号数据的组推荐	能够在不丢失信息的条件下从全局上把握数据特征；能对数据降维处理，减小算法复杂度；改善点数据的推荐算法，扩展其应用领域	源数据要求较高，依靠群组用户对某个项目进行精确打分；冷启动问题影响明显
基于信任及可信度的组推荐	通过结合信任、偏好和专长与社会信息相结合，用户群划分考虑更为全面；推荐结果可信度较高	易陷入盲目的信任问题；信任度易随时间推移发生改变
基于社区网络的组推荐	考虑成员间相似性和相互作用的影响，结合协同过滤，通过群体决策理论和权重对推荐结果进行排序，达到高精度推荐	源数据要求很高，如个人信息是否完善及信息的安全性和身份的不确定性
基于标签的组推荐	结合个人和项目标签，群组划分更为精准且全面，还能缓解数据稀疏性的问题，提高推荐效果	算法复杂度高，数量庞大时推荐的过程耗时长且效率较低

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