3 citations · 6 across the 13 of their papers we have counts for
13 papers
TimeLitmus: A Diagnostic Benchmark for Cross-Modal Understanding and Explanation Faithfulness in Event-Conditioned Time-Series Prediction
Jie Gong, Maowei Jiang, Zhiwei Liu +6
Large language models (LLMs) are increasingly used to make predictions from numerical time-series histories and textual events. Yet accuracy alone cannot reveal whether correct ans…
ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors
Jie Gong, Maowei Jiang, Zhiwei Liu +14
Conversational investment advisors influence not only what users know, but also how they make subsequent decisions as market conditions evolve. Existing evaluations primarily asses…
Give Users the Wheel: Towards Promptable Recommendation Paradigm
Fuyuan Lyu, Chenglin Luo, Qiyuan Zhang +6
Conventional sequential recommendation models have achieved remarkable success in mining implicit behavioral patterns. However, these architectures remain structurally blind to exp…
Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation
Yan Wang, Yi Han, Lingfei Qian +11
Most recommendation benchmarks evaluate how well a model imitates user behavior. In financial advisory, however, observed actions can be noisy or short-sighted under market volatil…
Exploring Test-time Scaling via Prediction Merging on Large-Scale Recommendation
Fuyuan Lyu, Zhentai Chen, Jingyan Jiang +4
Inspired by the success of language models (LM), scaling up deep learning recommendation systems (DLRS) has become a recent trend in the community. All previous methods tend to sca…
A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?
Qiyuan Zhang, Fuyuan Lyu, Zexu Sun +10
As enthusiasm for scaling computation (data and parameters) in the pretraining era gradually diminished, test-time scaling (TTS), also referred to as ``test-time computing'' has em…
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models
Kexin Zhang, Fuyuan Lyu, Xing Tang +5
The evolution of previous Click-Through Rate (CTR) models has mainly been driven by proposing complex components, whether shallow or deep, that are adept at modeling feature intera…
Mixed-Precision Embeddings for Large-Scale Recommendation Models
Shiwei Li, Zhuoqi Hu, Xing Tang +6
Embedding techniques have become essential components of large databases in the deep learning era. By encoding discrete entities, such as words, items, or graph nodes, into continu…
Collaborative Performance Prediction for Large Language Models
Qiyuan Zhang, Fuyuan Lyu, Xue Liu +1
Comprehensively understanding and accurately predicting the performance of large language models across diverse downstream tasks has emerged as a pivotal challenge in NLP research.…
Towards Automated Negative Sampling in Implicit Recommendation
Fuyuan Lyu, Yaochen Hu, Xing Tang +3
Negative sampling methods are vital in implicit recommendation models as they allow us to obtain negative instances from massive unlabeled data. Most existing approaches focus on s…
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
Fuyuan Lyu, Xing Tang, Dugang Liu +5
Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection i…
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Fuyuan Lyu, Xing Tang, Dugang Liu +4
Representation learning has been a critical topic in machine learning. In Click-through Rate Prediction, most features are represented as embedding vectors and learned simultaneous…
Optimizing Feature Set for Click-Through Rate Prediction
Fuyuan Lyu, Xing Tang, Dugang Liu +3
Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Ther…