원문정보
보안공학연구지원센터(IJUNESST)
International Journal of u- and e- Service, Science and Technology
Vol.7 No.4
2014.08
pp.53-62
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초록
영어
The analysis of the existing recommendation system and the main task in the electronic commerce application and the existing problems of the basis, according to the new user "cold start" problem, to adopt a user in a number of different categories of electronic commerce website access multi-B2C behavior information recommendation. This paper presents a crossing ranking recommendation algorithm. Its accuracy can be far more than the random recommendation, at the same time keeping and diversity were recommended. All these ensure the algorithm has a good user experience. Experiments show that the algorithm is accurate and the algorithm is further enhanced.
목차
Abstract
1. Introduction
2. Data Analysis of Multi-B2C Behavior
3. Crossing Ranking Recommendation Algorithm of Multi-B2C Behavior
3.1. The Recommendation Algorithm Based on Two Part Graph Resource Allocation
3.2. Recommendation List
4. Experimental Analysis and Results
4.1. Dataset Partition
4.2. Correlation Algorithm
4.3. The Experimental Results of One-to-One Crossing Ranking Recommended
4.4. The Experimental Results of Many-to-One Crossing Ranking Recommended
5. Conclusion
References
1. Introduction
2. Data Analysis of Multi-B2C Behavior
3. Crossing Ranking Recommendation Algorithm of Multi-B2C Behavior
3.1. The Recommendation Algorithm Based on Two Part Graph Resource Allocation
3.2. Recommendation List
4. Experimental Analysis and Results
4.1. Dataset Partition
4.2. Correlation Algorithm
4.3. The Experimental Results of One-to-One Crossing Ranking Recommended
4.4. The Experimental Results of Many-to-One Crossing Ranking Recommended
5. Conclusion
References
저자정보
참고문헌
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