Open Access
ARTICLE
Embedding Implicit User Importance for Group Recommendation
Qing Yang1, Shengjie Zhou1, Heyong Li1, Jingwei Zhang2, 3, *
1 Guangxi Key Laboratory of Automatic Measurement Technology and Instrument, Guilin University of
Electronic Technology, Guilin, 541004, China.
2 Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China.
3 Centre for Applied Informatics, Victoria University, Melbourne, 8001, Australia.
* Corresponding Author: Jingwei Zhang. Email: .
Computers, Materials & Continua 2020, 64(3), 1691-1704. https://doi.org/10.32604/cmc.2020.010256
Received 20 February 2020; Accepted 26 April 2020; Issue published 30 June 2020
Abstract
Group recommendations derive from a phenomenon in which people tend to
participate in activities together regardless of whether they are online or in reality, which
creates real scenarios and promotes the development of group recommendation systems.
Different from traditional personalized recommendation methods, which are concerned
only with the accuracy of recommendations for individuals, group recommendation is
expected to balance the needs of multiple users. Building a proper model for a group of
users to improve the quality of a recommended list and to achieve a better recommendation
has become a large challenge for group recommendation applications. Existing studies
often focus on explicit user characteristics, such as gender, occupation, and social status,
to analyze the importance of users for modeling group preferences. However, it is usually
difficult to obtain extra user information, especially for ad hoc groups. To this end, we
design a novel entropy-based method that extracts users’ implicit characteristics from
users’ historical ratings to obtain the weights of group members. These weights represent
user importance so that we can obtain group preferences according to user weights and
then model the group decision process to make a recommendation. We evaluate our method
for the two metrics of recommendation relevance and overall ratings of recommended
items. We compare our method to baselines, and experimental results show that our method
achieves a significant improvement in group recommendation performance.
Keywords
Cite This Article
Q. Yang, S. Zhou, H. Li and J. Zhang, "Embedding implicit user importance for group recommendation,"
Computers, Materials & Continua, vol. 64, no.3, pp. 1691–1704, 2020. https://doi.org/10.32604/cmc.2020.010256
Citations