Lina Yao

NeuRec: On Nonlinear Transformation for Personalized Ranking

2018

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity …

Spectrum-Guided Adversarial Disparity Learning

2020

It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class. In this …

Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning

2024

In this paper, we study personalized federated learning for text classification with Pretrained Language Models (PLMs).We identify two challenges in efficiently leveraging PLMs for personalized federated learning: 1) Communication.PLMs are usually large in size, inducing …

Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation

2025 · arXiv (Cornell University)

Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across domains to enhance recommendation quality. However, naive aggregation of sequential signals can introduce conflicting domain-specific preferences, leading to negative transfer. While Sequential Recommendation (SR) already suffers from …

CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing

2026

Rui Wang, Junda Wu, Yu Xia, Tong Yu, Ruiyi Zhang, Ryan A. Rossi, Subrata Mitra, Lina Yao, Julian McAuley. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). …

AutoSVD++

2017

Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such …

Deep Learning based Recommender System: A Survey and New Perspectives

2017 · arXiv (Cornell University)

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, …

DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns

2019

Cross-domain recommendation has long been one of the major topics in recommender systems.Recently, various deep models have been proposed to transfer the learned knowledge across domains, but most of them focus on extracting abstract transferable …

Deep Learning Based Recommender System

2019 · ACM Computing Surveys

With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along …