Leyu Lin

Real-time Attention Based Look-alike Model for Recommender System

2019

Recently, deep learning models play more and more important roles in contents recommender systems. However, although the performance of recommendations is greatly improved, the "Matthew effect" becomes increasingly evident. While the head contents get more …

Follow the Title Then Read the Article: Click-Guide Network for Dwell Time Prediction

2019 · IEEE Transactions on Knowledge and Data Engineering

In article recommendation, the amount of time user spends on viewing articles, dwell time, is an important metric to measure the post-click engagement of user on content and has been widely used as a proxy …

Denoising Relation Extraction from Document-level Distant Supervision

2020

Distant supervision (DS) has been widely used to generate auto-labeled data for sentencelevel relation extraction (RE), which improves RE performance. However, the existing success of DS cannot be directly transferred to the more challenging document-level …

Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

2021

Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary (source) domain to improve the performance of recommender system …

Multi-granularity Fatigue in Recommendation

2022 · Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Personalized recommendation aims to provide appropriate items according to user preferences mainly from their behaviors. Excessive homogeneous user behaviors on similar items will lead to fatigue, which may decrease user activeness and degrade user experience. …

Selective Fairness in Recommendation via Prompts

2022 · arXiv (Cornell University)

Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which …

Contrastive Cross-domain Recommendation in Matching

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-world systems. However, CDR in the matching (i.e., …