Integrating the importance levels of friends into trust-based ant-colony recommender systems
International Journal of Web Information Systems
ISSN: 1744-0084
Article publication date: 1 November 2018
Issue publication date: 7 March 2019
Abstract
Purpose
This paper aims to propose a trust-based ant-colony recommender system. It achieves high accuracy and coverage by integrating the importance level of friends. This paper has two main contributions, namely, selecting higher-quality raters and improving the prediction step. From these two contributions, the proposed trust-based ant-colony recommender system could provide more accurate and wider-coverage prediction than existing systems.
Design/methodology/approach
To obtain higher-quality raters, the data set was preprocessed, and then, trust values were calculated. The depth of search was increased to obtain higher coverage levels. This work also focuses on the importance level of friends in the system. Because the levels of influence on the active user of all friends are not equal, the importance level of friends is integrated into the system by transposing rater’s rating to the active user’s perspective and then assigning a weight to each rater.
Findings
The experimental evaluation clearly demonstrates that the proposed method achieves better results in terms of both accuracy and coverage than existing trust-based recommender systems. It was found that integrating the importance level of friends into the system, which transposes ratings and assigns weight to each user, can increase accuracy and coverage.
Originality/value
Existing trust-based ant-colony recommender systems do not consider the importance level of friends in the prediction step. Most of them only focus on finding raters and then using the rater’s real ratings in the prediction step. A new method is proposed that integrates the importance level of friends into the system by transposing a rater’s rating to match the active user’s perspective and assigning a weight for each rater. The experimental evaluation demonstrates that the proposed method achieves better accuracy and coverage than existing systems.
Keywords
Acknowledgements
This paper is an extended version of the work presented at the 18th International Conference on Information Integration and Web-based Applications and Services (iiWAS2016) (Tengkiattrakul et al., 2016). This work was supported by a Japan Society for the Promotion of Science grant-in-aid for Scientific Research 15H02789.
Citation
Tengkiattrakul, P., Maneeroj, S. and Takasu, A. (2019), "Integrating the importance levels of friends into trust-based ant-colony recommender systems", International Journal of Web Information Systems, Vol. 15 No. 1, pp. 28-46. https://doi.org/10.1108/IJWIS-02-2018-0009
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited