Work

Improving Collaborative Recommendation via Location-based User-item Subgroup

Year: 2014

Type: conference-paper

Abstract: Collaborative filter has been widely and successfully applied in recommendation system. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. Some previous studies have explored that there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items and subgroup analysis can get better accuracy. While, we find that geographical information of user have impacts on user group preference for items. Hence, In this paper, we propose a Bayesian generative model to describe the generative process of user-item subgroup preference under considering users’ geographical information. Experimental results show the superiority of the proposed model.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2014.05.036

Authors: Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo

Institutions: Chinese Academy of Sciences, Institute of Computing Technology, University of Chinese Academy of Sciences, Institute of Information Engineering

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Chinese Academy of Sciences

Awards: G201327168, 下一代大数据流分类系统研究