activity
20222024
most citedExplainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

46 citations · 117 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2024

To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process

Zhongxiang Sun, Zihua Si, Xiao Zhang +4

Incorporating Search and Recommendation (S&R) services within a singular application is prevalent in online platforms, leading to a new task termed open-app motivation prediction,…

cs.IR20242 cited

Logic Rules as Explanations for Legal Case Retrieval

Zhongxiang Sun, Kepu Zhang, Weijie Yu +2

In this paper, we address the issue of using logic rules to explain the results from legal case retrieval. The task is critical to legal case retrieval because the users (e.g., law…

cs.IR202330 cited

When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation

Zihua Si, Zhongxiang Sun, Xiao Zhang +5

Modern online service providers such as online shopping platforms often provide both search and recommendation (S&R) services to meet different user needs. Rarely has there been an…

cs.CL202339 cited

A Short Survey of Viewing Large Language Models in Legal Aspect

Zhongxiang Sun

Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. These models have also made a signifi…

cs.IR202246 cited

Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

Weijie Yu, Zhongxiang Sun, Jun Xu +4

As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching r…