Binbin Hu

Counterfactual Review-based Recommendation

2021

Incorporating review information into the recommender system has been demonstrated to be an effective method for boosting the recommendation performance. Previous research mainly focus on designing advanced architectures to better profile the users and items. …

Can Small Language Models be Good Reasoners for Sequential Recommendation?

2024 · arXiv (Cornell University)

Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are still numerous challenges that should be addressed to successfully implement sequential recommendations …

Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking

2024 · arXiv (Cornell University)

Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) …

How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective

2025

Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this bias but often exacerbate it, resulting in over-representation …

Comprehensive Evaluation Index System for Sustainable Development of Digital Museums: An Empirical Study Based on ACG–TSOBP Model and SDN–IoT Architectures

2025 · International Journal of High Speed Electronics and Systems

With the rapid digitalization of cultural heritage, evaluating the sustainability of digital museums remains a challenge. This study constructs a comprehensive evaluation index system encompassing economy, society, resources, services, and technology. To enhance evaluation accuracy …

Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model

2018

Heterogeneous information network (HIN) has been widely adopted in recommender systems due to its excellence in modeling complex context information. Although existing HIN based recommendation methods have achieved performance improvement to some extent, they have …

Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network

2018

Since heterogeneous information network (HIN) is able to integrate complex information and contain rich semantics, there is a surge of HIN based recommendation in recent years. Although existing methods have achieved performance improvement to some …

Heterogeneous Information Network Embedding for Recommendation

2018 · IEEE Transactions on Knowledge and Data Engineering

Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, calledHIN based recommendation. It is challenging to develop effective methods …