원문정보
초록
영어
With the rapid development of the Internet, for-profit site need to analyze the user's behavior and provide more satisfactory service. Therefore, the classification of network behavior analysis and the further research based on it are more and more urgent on the agenda. In this paper, a method based on similar aggregation user behavior analysis algorithm is proposed. Addressing the recommendation of personalized books problems is solved by this method. Firstly, the user behavior is analyzed by using the RFM model. Secondly, the Apriori algorithm based on weight increment is applied to mining association rules between users in line with the recent habits of users. Similarity is calculated by Apriori algorithm with using VSM model. In this paper, readers’ browsing history of e-library which is provided by Harbin University of Science and Technology is used as experimental data. This method is compared with the method which does not use the weight increment and similar aggregation. Comparison of results showed that the method of our paper can meet the requirements of the Book Recommendation system.
목차
1. Introduction
2. Books Recommended Process Based on User Behavior Analysis
3. User Behavior Analysis Algorithms Based on Hybrid Strategy
3.1. User Behavior Analysis Based on RFM Model
3.2. The Apriori Algorithm Based on Weight Increment
3.3. User Similar Aggregation based on VSM Model
3.4 Book Recommend Based on Collaborative Filtering
4. Experimental Validation and Analysis
4.1. The Construction of Experimental Platform
4.2. Experimental Results and Analysis
5. Conclusion
References