most citedExploration and Regularization of the Latent Action Space in Recommendation

41 citations · 105 across the 11 of their papers we have counts for

collaborators

11 papers

cs.IR20232 cited

Exploring Fine-tuning ChatGPT for News Recommendation

Xinyi Li, Yongfeng Zhang, Edward C Malthouse

News recommendation systems (RS) play a pivotal role in the current digital age, shaping how individuals access and engage with information. The fusion of natural language processi…

cs.IR20233 cited

A Content-Driven Micro-Video Recommendation Dataset at Scale

Yongxin Ni, Yu Cheng, Xiangyan Liu +5

Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and…

cs.IR2023

The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples

Ziheng Chen, Fabrizio Silvestri, Jia Wang +2

Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial int…

cs.IR20233 cited

PBNR: Prompt-based News Recommender System

Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

Online news platforms often use personalized news recommendation methods to help users discover articles that align with their interests. These methods typically predict a matching…

cs.CV202311 cited

HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention

Shijie Geng, Jianbo Yuan, Yu Tian +2

The success of large-scale contrastive vision-language pretraining (CLIP) has benefited both visual recognition and multimodal content understanding. The concise design brings CLIP…

cs.IR202341 cited

Exploration and Regularization of the Latent Action Space in Recommendation

Shuchang Liu, Qingpeng Cai, Bowen Sun +7

In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction.…