Abstract
Personalized recommendation service helping users to target the interesting information from the excessive information set has been widely concerned. In this paper, we firstly propose a new method named Modified TOPSIS Method utilizing the Improved Gray Correlation Analysis Method. Then, we present a new personalized recommendation technique based on the Modified TOPSIS Method. Finally, the verification method utilizing Spearman’s Rank Correlation Coefficient demonstrates that our new personalized recommendation technique is efficient.
Highlights
In such an Information Age, it appeals to be increasingly difficult for individuals to discover the useful information from the overwhelming amount of information available
We introduce weight of each index calculated by the Improved Gray Correlation Analysis to the Original TOPSIS Method for improvement
This paper proposes a new personalized recommendation technique utilizing the Modified TOPSIS Method
Summary
In such an Information Age, it appeals to be increasingly difficult for individuals to discover the useful information from the overwhelming amount of information available. There are mainly three types of personalized recommendation system, content-based filtering systems, collaborative filtering systems and data mining systems. Content-based recommendation is the development of collaborative filtering systems. It doesn’t need the assessment opinions from the users, which is too subjective and may limited by the insufficiency of users’ assessment information. Based the choices the users made before, it can calculate the similarity among users, and put forward recommendation. Base on the content-based filtering systems, our new personalized recommendation technique firstly requires users to choose the indices satisfying the customized needs of users. After gathering the data sets related to the selected indices, the ranking result calculated by the Modified TOPSIS Method will be eventually presented to users
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