English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Frequent group activities of human beings have become an indispensable partin their daily life. Group recommendation can recommend satisfactory activitiesto group members in the recommender systems, and the key issue is how toaggregate preferences in different group members. Most existing grouprecommendation employed the predefined static aggregation strategies toaggregate the preferences of different group members, but these staticstrategies cannot simulate the dynamic group decision-making. Meanwhile, mostof these methods depend on intuitions or assumptions to analyze the influenceof group members and lack of convincing theoretical support. We argue that theinfluence of group members plays a particularly important role in groupdecision-making and it can better assist group profile modeling and performmore accurate group recommendation. To tackle the issue of preferenceaggregation for group recommendation, we propose a novel attentive aggregationrepresentation learning method based on sociological theory for grouprecommendation, namely SIAGR (short for "Social Influence-based Attentive GroupRecommendation"), which takes attention mechanisms and the popular method(BERT) as the aggregation representation for group profile modeling.Specifically, we analyze the influence of group members based on socialidentity theory and two-step flow theory and exploit an attentive mavens miningmethod. In addition, we develop a BERT-based representation method to learn theinteraction of group members. Lastly, we complete the group recommendationunder the neural collaborative filtering framework and verify the effectivenessof the proposed method by experimenting.

Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation