人们群体活动对个性化推荐的实际需求，使得群体推荐系统的研究日益受到关注。考虑到符号数据分析（SDA）具有在不丢失信息前提下、从全局上把握数据特征的优势，研究基于SDA的群体推荐系统的用户建模和协调等关键问题。研究存在社会影响下的个体兴趣模型的构建；视群体为"符号对象"，运用SDA"数据打包"技术，将个体用户模型汇总，构建基于符号数据描述的群体用户模型。在SDA框架下，研究将传统的基于"点数据"描述的协同过滤等推荐算法扩展到符号数据情形；研究将符号数据多元分析方法应用于群体推荐算法。为使所有个体就推荐项目达成共识，评估待推荐项目与群体的所有成员用户偏好之间的一致性，并向用户对推荐结果进行解释，基于此研究用户之间的协调与谈判策略。研究群体满意度的度量，并建立以其为主要指标的群体推荐系统的综合评价体系。选取新兴的电子商务模式- - 网络团购，进行应用研究，并研究开发网络团购群体推荐信息系统。

The actual demand for providing recommendation to group activities attracts more and more attention on the research of group recommendation system. Considering the method of symbolic data analysis (SDA) has the advantage of mastering the data feature without losing any data information, this research focuses on user profiling and coordinating in group recommendation system based on SDA. The process of building the individual member's user profile is first addressed when existing social influence in the group members. Then, thinking the group as a symbolic object, this research proposes the method of aggregating individual profiles to a group profile based on symbolic data description, by using the data packing technology in SDA. In the framework of SDA, the research extends the traditional collaborative filtering algorithm among others described by point data to that of SDA, and applies some multivariate analysis methods of symbolic data in our group recommendation algorithms. This research also assesses the consistency between the items to be recommended and the preference of each members of the group, and explains recommendation results to users. To make all the individuals reach a consensus on the recommendation items, it studies the strategy of coordination and negotiation among the users. The research also put forward a comprehensive evaluation metrics with the measurement of group satisfaction as a core metric to evaluate our methods. Finally, this research conducts an application study on the burgeoning electronic commerce mode-Business to Team (B2T), and develops a group recommendation information system of B2T.