Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system

Pan, Yinghui, Huo, Yongfeng, Tang, Jing, Zeng, Yifeng and Chen, Bilian (2021) Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system. Information Sciences, 545. pp. 448-464. ISSN 0020-0255

[img] Text
STEM_.pdf - Accepted Version
Restricted to Repository staff only until 18 September 2021.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (1MB) | Request a copy
Official URL: https://doi.org/10.1016/j.ins.2020.09.001

Abstract

A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link the tags with corresponding resources, therefore leading to a static user profile in the recommendation. In this article, we have proposed a new social tag expansion model (STEM) to generate a dynamic user profile to improve the recommendation performance. Instead of simply including most relevant tags, the new model focuses on the completeness of a user profile through expanding tags by exploiting their relations and includes a sufficient set of tags to alleviate the tag sparsity problem. The novel STEM-based TRS contains three operations: (1) Tag cloud generation discovers potentially relevant tags in an application domain; (2) Tag expansion finds a sufficient set of tags upon original tags; and (3) User profile refactoring builds a dynamic user profile and determines the weights of the extended tags in the profile. We analysed the STEM property in terms of recommendation accuracy and demonstrated its performance through extensive experiments over multiple datasets. The analysis and experimental results showed that the new STEM technique was able to correctly find a sufficient set of tags and to improve the recommendation accuracy by solving the tag sparsity problem. At this point, this technique has consistently outperformed state-of-art tag-aware recommendation methods in these extensive experiments.

Item Type: Article
Uncontrolled Keywords: Tag expansion, Dynamic user profile, Bayesian networks, Recommender system
Subjects: G400 Computer Science
Department: Faculties > Business and Law > Newcastle Business School
Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 20 Oct 2020 11:18
Last Modified: 21 Oct 2020 08:30
URI: http://nrl.northumbria.ac.uk/id/eprint/44556

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics