A BERT Based Hybrid Recommendation System For Academic Collaboration

TL;DR

This paper presents a hybrid BERT-based recommendation system for academic collaboration that effectively connects university stakeholders, enhancing networking through an intelligent, context-aware mobile application.

Contribution

It introduces a novel hybrid recommendation approach combining BERT and clustering techniques, optimized for unlabelled academic profile data, and implements it as a mobile app.

Findings

Hybrid model outperforms individual techniques in recommendation quality

Clustering improves understanding of profile groupings

Mobile app effectively suggests relevant academic profiles

Abstract

Universities serve as a hub for academic collaboration, promoting the exchange of diverse ideas and perspectives among students and faculty through interdisciplinary dialogue. However, as universities expand in size, conventional networking approaches via student chapters, class groups, and faculty committees become cumbersome. To address this challenge, an academia-specific profile recommendation system is proposed to connect like-minded stakeholders within any university community. This study evaluates three techniques: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid approach to generate effective recommendations. Due to the unlabelled nature of the dataset, Affinity Propagation cluster-based relabelling is performed to understand the grouping of similar profiles. The hybrid model demonstrated superior performance, evidenced by its similarity score, Silhouette score, Davies-Bouldin index, and Normalized Discounted Cumulative Gain (NDCG), achieving an optimal balance between diversity and relevance in recommendations. Furthermore, the optimal model has been implemented as a mobile application, which dynamically suggests relevant profiles based on users' skills and collaboration interests, incorporating contextual understanding. The potential impact of this application is significant, as it promises to enhance networking opportunities within large academic institutions through the deployment of intelligent recommendation systems.