The recommendation system, as a widely used and effective tool to alleviate information overload, has been receiving increasing attention regarding its issues of bias and fairness. Many studies have focused on addressing fairness on the item side, targeting item fairness by minimizing exposure discrepancies of items among similar individuals. However, in real-world recommendation scenarios, many relevant items requiring similar exposure to users may exhibit certain dissimilarities while existing methods could not solve the problem. To address this, we define a broader item fairness recommendation issue aimed at improving fairness within specified groups of related individual items, which we term as “intra-group item fairness”. To solve this issue, we propose a Group-oriented Individual Fairness recommendation model called GIFRec. First, we introduce a global exposure balance module to mitigate exposure imbalances at a global level, with the help of multimodal information contained in each item. Then, at the group level, we propose a group fusion embedding representation method, allowing individual items within the same group to adaptively share group information. Additionally, as unfair training opportunities may arise for different items during model training, we propose a general fair intra-group optimization method to reduce individual training biases within the same group. Extensive experiments conducted on four real-world datasets demonstrate the effectiveness of our approach with an average improvement of 11.27% in accuracy and 24.91% in fair ness compared to eight SOTA methods. Our implementation and some details can be found at https://github.com/xderui/GIFRec.

In today’s information-rich era, users rely heavily on recommender systems to identify relevant content. Graph structures, renowned for their ability to model intricate user-content relationships, have become essential to these systems. However, the accuracy of recommendations hinges critically on the quality of node representations within these graphs. Personalized recommendations strive to enhance uniqueness by maximizing the dissimilarity between representations (known as uniformity) while simultaneously ensuring that the representations align closely with the content users engage with (dubbed as alignment). Nevertheless, balancing these conflicting objectives remains a challenge for optimal recommendation performance. To tackle these challenges, we propose an innovative approach called SIURec, which differs significantly from previous studies. Rather than relying on manual weight selection between uniformity and alignment and optimizing uniformity solely on the final representation, SIURec adopts an adaptive adjustment method that learns the optimal weight between uniformity and alignment automatically. By optimizing uniformity at every convolutional layer, SIURec captures users’ sub-interests more effectively, ultimately leading to improved recommendation accuracy. Experimental results on four datasets demonstrate that SIURec achieves superior learning of uniformity (with an average improvement of 4.26% in accuracy compared to eleven SOTA methods) and exhibits robustness across different hyperparameter settings.

Today, mainstream recommendation systems have achieved remarkable success in recommending items that align with user interests. However, limited attention has been paid to the perspective of item providers. Content providers often desire that all their offerings, including unpopular or cold items, aredisplayed and appreciated by users. To tackle the challenges ofunfair exhibition and limited item acceptance coverage, we introduce a novel recommendation perspective that enables items to “select” their most relevant users. We further introduce ItemRec, a straightforward plug-and-play approach that leverages mutual scores calculated by any model. The goal is to maximize the recommendation and acceptance of items by users. Through extensive experiments on three real-world datasets, we demonstrate that ItemRec can enhance valid coverage by up to 38.5% while maintaining comparable or superior recommendation quality. This improvement comes with only a minor increase in model inference time, ranging from 1.5% to 5%. Furthermore, when compared to thirteen state-of-the-art recommendation methods across accuracy, fairness, and diversity, ItemRec exhibits significant advantages as well. Specifically, ItemRec achieves an optimal balance between precision and valid coverage, showcasing an efficiency gain ranging from 1.8 to 45 times compared to other fairness-oriented methodologies.