With the rapid proliferation of short-video platforms and content-driven social networks, sequential recommendation models capable of accurately capturing user interests have become increasingly crucial. Among these, Transformer-based sequential recommendation models have gained widespread adoption due to their superior ability. The positional encoding (PE) in Transformer architectures serves to incorporate positional information into sequences. However, relying solely on original absolute positional information may be insufficient for sequential recommendation models. In contrast, the dwell time after interactions (i.e., the time intervals between consecutive user interactions) provides a more accurate reflection of users' emotional responses and evolving interests. Despite its significance, this aspect has often been overlooked in existing works. To fully utilize this information, our work introduces an adaptive PE method, termed TSAPE (Temporal-Series-Aware Positional Encoding). This approach introduces an innovative modeling of the sequence of time intervals between user interactions, rather than the numerical values of the intervals themselves, thereby capturing real-time feedback on user interests and integrating it with conventional PE mechanisms. Furthermore, we employ multiple layers of one-dimensional convolutional networks and attention mechanisms to endow the features with adaptive capabilities across various time interval scenarios. This enables TSAPE to more accurately capture sequential positional information at any given moment. By enhancing the sequential order information of interactions, TSAPE significantly improves the accuracy of next-item recommendations. We seamlessly integrated our method into several Transformer-based sequential recommendation models and conducted comparisons with state-of-the-art sequential recommendation approaches and widely-used PE methods. The results demonstrate that the integration of TSAPE consistently outperforms the original backbone models and other SOTA methods. The SASRec model integrated with TSAPE achieves an average improvement of 15.61% across three evaluation metrics on four benchmark datasets. Our code has been made publicly available at https://github.com/rongbo-qi/TSAPE_Rec.

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.

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.