Online Group Recommendation with Local Optimization
作者机构：School of Computer Science and EngineeringJiangsu University of Science and TechnologyZhenjiangChina
出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)
年 卷 期：2018年第115卷第5期
页 面：217-231页
核心收录：
学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
主 题：Group recommendation local optimization social network
摘 要：There are some scenarios that need group recommendation such as watching a movie or a TV series,selecting a tourist destination,or having dinner *** in this domain can be divided into two categories:Creating group profiles and aggregating individual recommender *** none of the above methods can handle the online group recommendation both efficiently and accurately and these methods either strongly limited by their application environment,or bring bias towards those users having limited connections with this *** this work,we propose a local optimization framework,using sub-group profiles to compute the item *** method can captures and removes the bias existed in the traditional group recommendation algorithms in a certain *** can then be used to derive single-user *** also propose an approach to overcome the problem caused by dynamic change or user updating about his social network,which detects the target user’s group by analyzing the link types between he and his neighbours,and then use the group information to generate his *** analysis for group and personal recommendation on three different sizes of MovieLens datasets show fairly good results,our method consistently outperform several state-of-the-arts in *** we also provide the explanations behind the phenomena during the experiments.
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