A Triangular Personalized Recommendation Algorithm for Improving Diversity
Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted c...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2018/3162068 |
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| _version_ | 1832545741436878848 |
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| author | Biao Cai Xiaowang Yang Yusheng Huang Hongjun Li Qiang Sang |
| author_facet | Biao Cai Xiaowang Yang Yusheng Huang Hongjun Li Qiang Sang |
| author_sort | Biao Cai |
| collection | DOAJ |
| description | Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods. |
| format | Article |
| id | doaj-art-cf14d24205c44c39b2a11650d613ba86 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-cf14d24205c44c39b2a11650d613ba862025-02-03T07:24:51ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2018-01-01201810.1155/2018/31620683162068A Triangular Personalized Recommendation Algorithm for Improving DiversityBiao Cai0Xiaowang Yang1Yusheng Huang2Hongjun Li3Qiang Sang4Department of Digital Media Technology, College of Information Science & Technology, Chengdu University of Technology, Chengdu, Sichuan, ChinaCollege of Information Science & Technology, Chengdu University of Technology, Chengdu, Sichuan, ChinaCollege of Information Science & Technology, Chengdu University of Technology, Chengdu, Sichuan, ChinaDepartment of Digital Media Technology, College of Information Science & Technology, Chengdu University of Technology, Chengdu, Sichuan, ChinaDepartment of Digital Media Technology, College of Information Science & Technology, Chengdu University of Technology, Chengdu, Sichuan, ChinaRecommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods.http://dx.doi.org/10.1155/2018/3162068 |
| spellingShingle | Biao Cai Xiaowang Yang Yusheng Huang Hongjun Li Qiang Sang A Triangular Personalized Recommendation Algorithm for Improving Diversity Discrete Dynamics in Nature and Society |
| title | A Triangular Personalized Recommendation Algorithm for Improving Diversity |
| title_full | A Triangular Personalized Recommendation Algorithm for Improving Diversity |
| title_fullStr | A Triangular Personalized Recommendation Algorithm for Improving Diversity |
| title_full_unstemmed | A Triangular Personalized Recommendation Algorithm for Improving Diversity |
| title_short | A Triangular Personalized Recommendation Algorithm for Improving Diversity |
| title_sort | triangular personalized recommendation algorithm for improving diversity |
| url | http://dx.doi.org/10.1155/2018/3162068 |
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