@Article{cmc.2020.010256,
AUTHOR = {Qing Yang, Shengjie Zhou, Heyong Li, Jingwei Zhang},
TITLE = {Embedding Implicit User Importance for Group  Recommendation},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1691--1704},
URL = {http://www.techscience.com/cmc/v64n3/39453},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010256}
}