A Triangular Personalized Recommendation Algorithm for Improving Diversity
Joint Authors
Cai, Biao
Yang, Xiaowang
Huang, Yusheng
Li, Hongjun
Sang, Qiang
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-27
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Cai, Biao& Yang, Xiaowang& Huang, Yusheng& Li, Hongjun& Sang, Qiang. 2018. A Triangular Personalized Recommendation Algorithm for Improving Diversity. Discrete Dynamics in Nature and Society،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1152462
Modern Language Association (MLA)
Cai, Biao…[et al.]. A Triangular Personalized Recommendation Algorithm for Improving Diversity. Discrete Dynamics in Nature and Society No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1152462
American Medical Association (AMA)
Cai, Biao& Yang, Xiaowang& Huang, Yusheng& Li, Hongjun& Sang, Qiang. A Triangular Personalized Recommendation Algorithm for Improving Diversity. Discrete Dynamics in Nature and Society. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1152462
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1152462