Jiarui Jin

GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network

2020 · arXiv (Cornell University)

Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path reachability to leverage path-based semantic relatedness or graph neighborhood to …

Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation

2023 · arXiv (Cornell University)

With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning …

DREAM: A Dual Representation Learning Model for Multimodal Recommendation

2024 · arXiv (Cornell University)

Multimodal recommendation focuses primarily on effectively exploiting both behavioral and multimodal information for the recommendation task. However, most existing models suffer from the following issues when fusing information from two different domains: (1) Previous works …

User Behavior Retrieval for Click-Through Rate Prediction

2020

Click-through rate (CTR) prediction plays a key role in modern online personalization services. In practice, it is necessary to capture user's drifting interests by modeling sequential user behaviors to build an accurate CTR prediction model. …

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

2020

There is an influx of heterogeneous information network (HIN) based recommender systems in recent years since HIN is capable of characterizing complex graphs and contains rich semantics. Although the existing approaches have achieved performance improvement, …