Huawei Shen

Locate Who You Are: Matching Geo-location to Text for Anchor Link Prediction.

2021 · arXiv (Cornell University)

Nowadays, users are encouraged to activate across multiple online social networks simultaneously. Anchor link prediction, which aims to reveal the correspondence among different accounts of the same user across networks, has been regarded as a …

LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback

2022 · arXiv (Cornell University)

Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF …

RLKD: Distilling LLMs' Reasoning via Reinforcement Learning

2025 · arXiv (Cornell University)

Distilling reasoning paths from teacher to student models via supervised fine-tuning (SFT) provides a shortcut for improving the reasoning ability of smaller Large Language Models (LLMs). However, the reasoning paths generated by teacher models often …

Cross-Domain Recommendation: An Embedding and Mapping Approach

2017

Data sparsity is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation, i.e., leveraging feedbacks or ratings from multiple domains to improve recommendation performance in a …

Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation

2018

Point-of-interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods …