Improve top-k recommendation by extending review analysis

Authors:
Qing Zhu;Zhe Xing;JingFan Liang
Affiliations:
School of Information, Renmin University of China, Beijing, China and Key Laboratory for Data Engineering and Knowledge Engineering MOE, Renmin University of China, Beijing, China;School of Information, Renmin University of China, Beijing, China and Key Laboratory for Data Engineering and Knowledge Engineering MOE, Renmin University of China, Beijing, China;School of Information, Renmin University of China, Beijing, China and Key Laboratory for Data Engineering and Knowledge Engineering MOE, Renmin University of China, Beijing, China
Venue:
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Year:
2012

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Abstract

The Web has become the popular place for people to purchase product and acquire services, so collaborative filtering is one of the most important algorithms applied in e-commerce recommendation systems. Unfortunately, it is widely recognized that the traditional recommendation methods are inefficient when the user rating data is extremely sparse. In order to overcome the limitations, good recommendation tools are needed to help Web customers determine the products and satisfaction services. In this paper, we propose a multi-dimensional adaptive recommendation algorithm by extending opinion analysis to improve top-k recommendation. In the first step, the novel algorithm that uses extened opinion analysis, creatively combines three dimensional recommendation models: user-based, item-based and opinion-based collaborative filtering. It successfully integrates opinion mining technology with collaborative filtering algorithm. In the second step, we configured the dynamic measurement would help us determine the weight of three dimensions: user-based, item-based and opinion-based analysis, and hence get the final prediction result. The experimental results show that multi-dimensional recommendation can effectively alleviate the dataset sparsity problem and achieve better prediction accuracy compared to other traditional collaborative recommendation algorithms.