Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks
By providing personalized suggestions to users, recommender systems have become
essential to numerous online platforms. Collaborative filtering, particularly graph-based …
essential to numerous online platforms. Collaborative filtering, particularly graph-based …
Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and Blockchain
Recommender systems (RS) play an integral role in many online platforms. Exponential
growth and potential commercial interests are raising significant concerns around privacy …
growth and potential commercial interests are raising significant concerns around privacy …
Pacer: Network embedding from positional to structural
Network embedding plays an important role in a variety of social network applications.
Existing network embedding methods, explicitly or implicitly, can be categorized into …
Existing network embedding methods, explicitly or implicitly, can be categorized into …
Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation
Online dating platforms have gained widespread popularity as a means for individuals to
seek potential romantic relationships. While recommender systems have been designed to …
seek potential romantic relationships. While recommender systems have been designed to …
A topological perspective on demystifying gnn-based link prediction performance
Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for
link prediction (LP). While numerous studies aim to improve the overall LP performance of …
link prediction (LP). While numerous studies aim to improve the overall LP performance of …
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness
Recommender systems (RSs) have gained widespread applications across various
domains owing to the superior ability to capture users' interests. However, the complexity …
domains owing to the superior ability to capture users' interests. However, the complexity …
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?
Next basket recommendation (NBR) is a special type of sequential recommendation that is
increasingly receiving attention. So far, most NBR studies have focused on optimizing the …
increasingly receiving attention. So far, most NBR studies have focused on optimizing the …
Diversifying Sequential Recommendation with Retrospective and Prospective Transformers
Previous studies on sequential recommendation (SR) have predominantly concentrated on
optimizing recommendation accuracy. However, there remains a significant gap in …
optimizing recommendation accuracy. However, there remains a significant gap in …
RosePO: Aligning LLM-based Recommenders with Human Values
Recently, there has been a growing interest in leveraging Large Language Models (LLMs)
for recommendation systems, which usually adapt a pre-trained LLM to the recommendation …
for recommendation systems, which usually adapt a pre-trained LLM to the recommendation …
On Evaluation Metrics for Diversity-enhanced Recommendations
Diversity is increasingly recognized as a crucial factor in recommendation systems for
enhancing user satisfaction. However, existing studies on diversity-enhanced …
enhancing user satisfaction. However, existing studies on diversity-enhanced …