7 papers
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…
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…
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…