353 citations · 513 across the 7 of their papers we have counts for
7 papers
UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches
Chao Wang, Neo Wu, Lin Ning +5
Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user info…
RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs
Jiaxing Wu, Lin Ning, Luyang Liu +7
LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users' behavior from their past activities. However, their effectiveness often hinges on th…
User-LLM: Efficient LLM Contextualization with User Embeddings
Lin Ning, Luyang Liu, Jiaxing Wu +6
Large language models (LLMs) have achieved remarkable success across various domains, but effectively incorporating complex and potentially noisy user timeline data into LLMs remai…
Massively Scalable Inverse Reinforcement Learning in Google Maps
Matt Barnes, Matthew Abueg, Oliver F. Lange +5
Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addre…
Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services
Luyang Liu, David Racz, Kara Vaillancourt +9
Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which…
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Amol Kapoor, Xue Ben, Luyang Liu +4
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecastin…
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
Neo Wu, Bradley Green, Xue Ben +1
In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a cru…