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Aiming to recommend potential collaborators for academic entities such as researchers and institutions, this paper develops a social recommender system through bibliometric indicators and network analytics. Targeting to scholarly articles, the proposed recommender system exploits co-authorships as established social relations and proposes a link prediction model for discovering such potential relations in terms of a co-authorship network. A case study recommending scientific collaborators for research entities on generelated diseases demonstrates the reliability of this study.

In the fast pace of life, E-learning has become a new way for self-improvement and competitiveness. The recommendation is needed in an E-learning system to filter suitable courses for users when they are facing a massive amount of information in course enrolment. However, due to the complexity of each learning course and the change of user interest, it is challenging to provide accurate recommendations. This paper proposes an E-learning recommender system that combines the recurrent neural network (RNN) and content-based technique to support users in course selection. The content-based techniques are to mine the relationships between courses, and the recurrent neural network is to extract user interests with a series of his/her enrolled courses. The proposed E-learning recommender system framework takes sequential connections into consideration. It intends to provide students with more precise course recommendations. The system is implemented with the Django framework and ElephantSQL cloud database and deployed on the Amazon Elastic Compute Cloud.

Accurate user preferences and item representations are essential factors for personalized recommender systems. Explicit feedback behaviors, such as ratings and free-text comments, are rich in personalized preference knowledge and emotional evaluation information. It is a direct and effective way to obtain individualized preference and item latent representations from these sources. In this paper, we propose a novel neural model named BERT-RS for personalized recommender systems, which extracts knowledge from textual reviews and user-item interactions. First, we preliminary extract the semantic representation for users and items from the textual comments based on BERT. Next, these semantic embeddings are used for user and item latent representations through three different deep architectures. Finally, we carry out personalized recommendation tasks through the score prediction based on these representations. Compared with other algorithms, BERT-RS demonstrates outstanding experimental performance on the Amazon dataset.

This talk will introduce some popular recommendation approaches and, in particular, present the recent developments by our Decision Systems and e-Service Intelligence (DeSI) lab in personalised recommender systems and their applications. It consists of three main parts: the models and approaches of recommender systems, the applications of soft computing in recommender systems, and recent developments of recommender system applications in e-business intelligence.