109 citations · 410 across the 44 of their papers we have counts for
10 papers · 1 filter
Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval
Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Armin Toroghi +6
As natural language interfaces enable users to express increasingly complex natural language queries, there is a parallel explosion of user review content that can allow users to b…
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
Scott Sanner, Krisztian Balog, Filip Radlinski +2
Traditional recommender systems leverage users' item preference history to recommend novel content that users may like. However, modern dialog interfaces that allow users to expres…
DiffuDetox: A Mixed Diffusion Model for Text Detoxification
Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya +4
Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text. It is highly useful for online forums and social media, where offensiv…
Bayesian Knowledge-driven Critiquing with Indirect Evidence
Armin Toroghi, Griffin Floto, Zhenwei Tang +1
Conversational recommender systems (CRS) enhance the expressivity and personalization of recommendations through multiple turns of user-system interaction. Critiquing is a well-kno…
Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction
Ta Jiun Ting, Xiaocan Li, Scott Sanner +1
Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent transportation systems. Today, graph convolutional neural networks (GCNNs) have b…
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization
Xiaocan Li, Ray Coden Mercurius, Ayal Taitler +4
Perimeter control maintains high traffic efficiency within protected regions by controlling transfer flows among regions to ensure that their traffic densities are below critical v…