Research on E-Commerce Platform-Based Personalized Recommendation Algorithm
Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2016/5160460 |
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| _version_ | 1832563993444614144 |
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| author | Zhijun Zhang Gongwen Xu Pengfei Zhang |
| author_facet | Zhijun Zhang Gongwen Xu Pengfei Zhang |
| author_sort | Zhijun Zhang |
| collection | DOAJ |
| description | Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms. |
| format | Article |
| id | doaj-art-69f32ef601ed43ecbefeed69459e7b97 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-69f32ef601ed43ecbefeed69459e7b972025-02-03T01:12:05ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322016-01-01201610.1155/2016/51604605160460Research on E-Commerce Platform-Based Personalized Recommendation AlgorithmZhijun Zhang0Gongwen Xu1Pengfei Zhang2School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong 250101, ChinaSchool of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong 250101, ChinaSchool of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong 250101, ChinaAiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.http://dx.doi.org/10.1155/2016/5160460 |
| spellingShingle | Zhijun Zhang Gongwen Xu Pengfei Zhang Research on E-Commerce Platform-Based Personalized Recommendation Algorithm Applied Computational Intelligence and Soft Computing |
| title | Research on E-Commerce Platform-Based Personalized Recommendation Algorithm |
| title_full | Research on E-Commerce Platform-Based Personalized Recommendation Algorithm |
| title_fullStr | Research on E-Commerce Platform-Based Personalized Recommendation Algorithm |
| title_full_unstemmed | Research on E-Commerce Platform-Based Personalized Recommendation Algorithm |
| title_short | Research on E-Commerce Platform-Based Personalized Recommendation Algorithm |
| title_sort | research on e commerce platform based personalized recommendation algorithm |
| url | http://dx.doi.org/10.1155/2016/5160460 |
| work_keys_str_mv | AT zhijunzhang researchonecommerceplatformbasedpersonalizedrecommendationalgorithm AT gongwenxu researchonecommerceplatformbasedpersonalizedrecommendationalgorithm AT pengfeizhang researchonecommerceplatformbasedpersonalizedrecommendationalgorithm |