An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model
With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system us...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-10-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/9/10/216 |
| _version_ | 1818038766381563904 |
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| author | Yan Guo Minxi Wang Xin Li |
| author_facet | Yan Guo Minxi Wang Xin Li |
| author_sort | Yan Guo |
| collection | DOAJ |
| description | With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer’s feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems. |
| first_indexed | 2024-12-10T07:47:57Z |
| format | Article |
| id | doaj.art-8eed3f205b1645ad9c01094ed694dbb7 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-12-10T07:47:57Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-8eed3f205b1645ad9c01094ed694dbb72022-12-22T01:57:08ZengMDPI AGSymmetry2073-89942017-10-0191021610.3390/sym9100216sym9100216An Interactive Personalized Recommendation System Using the Hybrid Algorithm ModelYan Guo0Minxi Wang1Xin Li2College of Management Science, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Management Science, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Management Science, Chengdu University of Technology, Chengdu 610059, ChinaWith the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer’s feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.https://www.mdpi.com/2073-8994/9/10/216e-commercerecommendation systeminteractive assignhybrid algorithmdata mining |
| spellingShingle | Yan Guo Minxi Wang Xin Li An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model Symmetry e-commerce recommendation system interactive assign hybrid algorithm data mining |
| title | An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model |
| title_full | An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model |
| title_fullStr | An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model |
| title_full_unstemmed | An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model |
| title_short | An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model |
| title_sort | interactive personalized recommendation system using the hybrid algorithm model |
| topic | e-commerce recommendation system interactive assign hybrid algorithm data mining |
| url | https://www.mdpi.com/2073-8994/9/10/216 |
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