papers
Publications (3)
cs.IR2023
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
Yueqi Wang, Yoni Halpern, Shuo Chang +9
Sequential recommenders have been widely used in industry due to their strength in modeling user preferences. While these models excel at learning a user's positive interests, less…
cs.CV2018
Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe
Jiong Gong, Haihao Shen, Guoming Zhang +6
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications. This paper presents the efficient inference techniq…
cs.IR2024
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
Chih-Wei Hsu, Martin Mladenov, Ofer Meshi +8
Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold s…