Group Semantic Recommendation System using Attention Neural Network

Abstract

Recommender systems (RS) are commonly used in areas such as online orders, travel, and music to suggest items that match user interests. With the rapid growth of social interactions and online activity, their use has naturally extended to both personal and Group Recommendation Systems. A group recommender system focuses on providing recommendations based on a group's shared preferences, rather than relying on a single individual’s choices. To overcome these challenges, we propose a novel BADLGRS developed. The GRL model was used to construct a tripartite graph representing interactions between the number of items, users, and group interactions. To effectively capture semantic group features, this phase introduces a novel model, GRUANN. The GCN model with two layers was used to learn user preferences under the GPL. A novel BADLGRS model is evaluated across four datasets and demonstrates superior performance when compared to existing methods. Specifically, it achieves accuracies of 0.893, 0.567, and 0.095, and a MAP of 0.095. Finally, the results of the BADLGRS model consistently outperform existing models in group recommendation tasks.