6 papers
Bringing GRACE to Recommendation: Fine-Tuning for Sustainable and Accurate Personalization
Yibowen Zhao, Yinan Zhang, Ning Liu +2
Growing concern about environmental sustainability (e.g., reducing carbon emissions and resource use) and public health has motivated ``green'' recommender systems that steer users…
Emulating Clinician Cognition via Self-Evolving Deep Clinical Research
Ruiyang Ren, Yuhao Wang, Yunsen Liang +8
Clinical diagnosis is a complex cognitive process, grounded in dynamic cue acquisition and continuous expertise accumulation. Yet most current artificial intelligence (AI) systems…
Following the TRAIL: Predicting and Explaining Tomorrow's Hits with a Fine-Tuned LLM
Yinan Zhang, Zhixi Chen, Jiazheng Jing +1
Large Language Models (LLMs) have been widely applied across multiple domains for their broad knowledge and strong reasoning capabilities. However, applying them to recommendation…
Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes
Yibowen Zhao, Yinan Zhang, Zhixiang Su +2
Predicting diseases solely from patient-side information, such as demographics and self-reported symptoms, has attracted significant research attention due to its potential to enha…
Bites of Tomorrow: Personalized Recommendations for a Healthier and Greener Plate
Jiazheng Jing, Yinan Zhang, Chunyan Miao
The recent emergence of extreme climate events has significantly raised awareness about sustainable living. In addition to developing energy-saving materials and technologies, exis…
Does Multimodality Improve Recommender Systems as Expected? A Critical Analysis and Future Directions
Hongyu Zhou, Yinan Zhang, Aixin Sun +1
Multimodal recommendation systems are increasingly popular for their potential to improve performance by integrating diverse data types. However, the actual benefits of this integr…