9 citations · 20 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
Jiazheng Jing, Yinan Zhang, Xin Zhou +1
Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through…
Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference
Yinan Zhang, Boyang Li, Yong Liu +2
Conversational recommendation system (CRS) is emerging as a user-friendly way to capture users' dynamic preferences over candidate items and attributes. Multi-shot CRS is designed…
Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
Yinan Zhang, Boyang Li, Yong Liu +2
Proper initialization is crucial to the optimization and the generalization of neural networks. However, most existing neural recommendation systems initialize the user and item em…