Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people’s daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is whether recommendations are fair. Unfair recommendations are not only unethical but also harm the long-term interests of the recommender system itself. As a result, fairness issues in recommender systems have recently attracted increasing attention. However, due to multiple complex resource allocation processes and various fairness definitions, the research on fairness in recommendation is scattered. To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW. First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues. Then, we review recommendation datasets and measurements in fairness studies and provide an elaborate taxonomy of fairness methods in the recommendation. Finally, we conclude this survey by outlining some promising future directions.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image–language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP’s accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in recommendation systems, we introduce MACRec, a novel framework designed to enhance recommendation systems through multi-agent collaboration. Unlike existing work on using agents for user/item simulation, we aim to deploy multi-agents to tackle recommendation tasks directly. In our framework, recommendation tasks are addressed through the collaborative efforts of various specialized agents, including Manager, User/Item Analyst, Reflector, Searcher, and Task Interpreter, with different working flows. Furthermore, we provide application examples of how developers can easily use MACRec on various recommendation tasks, including rating prediction, sequential recommendation, conversational recommendation, and explanation generation of recommendation results. The framework and demonstration video are publicly available at https://github.com/wzf2000/MACRec.

Adolescents with obesity face numerous health risks and encounter barriers that lead to physical inactivity. We developed a virtual reality sports system, named REVERIE (Real-World Exercise and VR-Based Exercise Research in Education), which used deep reinforcement learning to train transformer-based virtual coaching agents, offering immersive and effective sports guidance, with biomechanical performance comparable to real-world physical sports. We integrated REVERIE into a randomized controlled trial involving an 8-week intervention in adolescents with excess body weight (n = 227). Participants were randomized (1:1:1:1:1) to physical table tennis, physical soccer, REVERIE table tennis, REVERIE soccer or control. REVERIE sports intervention was effective in reducing primary outcome fat mass (mean −4.28 kg (95% confidence interval (CI) −6.35 to −2.22), relative to control), with no significant difference compared with physical sports (mean −5.06 kg (95% CI −7.13 to −2.98), relative to control). For secondary outcomes, decreases in liver enzymes and low-density lipoprotein cholesterol levels were found in physical and REVERIE sports groups compared to control. Physical and REVERIE sports showed improvements in physical fitness, psychological well-being and sports willingness after an 8-week intervention, which remained at the 6-month follow-up in the REVERIE sports group. REVERIE sports demonstrated superior cognitive enhancements compared to physical sports in exploratory analyses, as evidenced by olfactory tests (total score: mean 2.84 (95% CI 1.15 to 4.53)) and working memory paradigm (2-back accuracy: mean 10.88% (95% CI 1.19% to 20.56%)). Functional magnetic resonance imaging exhibited that REVERIE sports enhanced neural efficiency and neuroplasticity. Multi-omics analyses revealed distinct changes induced by REVERIE sports that were closely associated with cognitive improvement. Minimal injury rates were 7.69% for REVERIE and 13.48% for physical sports, with no serious adverse events. Collectively, this study demonstrates that the virtual reality sports therapy could provide an empathetic approach to addressing adolescent obesity, offering comprehensive improvements in physical, psychological and cognitive health beyond mere weight loss. Clinical Trial registration: ChiCTR2300068786.

As recommender systems become increasingly important in daily human decision-making, users are demanding convincing explanations to understand why they get the specific recommendation results. Although a number of explainable recommender systems have recently been proposed, there still lacks an understanding of what users really need in a recommendation explanation. The actual reason behind users’ intention to examine and consume (e.g., click and watch a movie) can be the window to answer this question and is named as self-explanation in this work. In addition, humans usually make recommendations accompanied by explanations, but there remain fewer studies on how humans explain and what we can learn from human-generated explanations. To investigate these questions, we conduct a novel multi-role, multi-session user study in which users interact with multiple types of system-generated explanations as well as human-generated explanations, namely peer-explanation . During the study, users’ intentions, expectations, and experiences are tracked in several phases, including before and after the users are presented with an explanation and after the content is examined. Through comprehensive investigations, three main findings have been made: First, we observe not only the positive but also the negative effects of explanations, and the impact varies across different types of explanations. Moreover, human-generated explanation, peer-explanation , performs better in increasing user intentions and helping users to better construct preferences, which results in better user satisfaction. Second, based on users’ self-explanation , the information accuracy is measured and found to be a major factor associated with user satisfaction. Some other factors, such as unfamiliarity and similarity, are also discovered and summarized. Third, through annotations of the information aspects used in the human-generated self-explanation and peer-explanation , patterns of how humans explain are investigated, including what information and how much information is utilized. In addition, based on the findings, a human-inspired explanation approach is proposed and found to increase user satisfaction, revealing the potential improvement of further incorporating more human patterns in recommendation explanations. These findings have shed light on the deeper understanding of the recommendation explanation and further research on its evaluation and generation. Furthermore, the collected data, including human-generated explanations by both the external peers and the users’ selves, will be released to support future research works on explanation evaluation.