3 citations · 8 across the 17 of their papers we have counts for
4 papers · 1 filter
Large Language Model as Universal Retriever in Industrial-Scale Recommender System
Junguang Jiang, Yanwen Huang, Bin Liu +6
In real-world recommender systems, different retrieval objectives are typically addressed using task-specific datasets with carefully designed model architectures. We demonstrate t…
Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning
Gang Zhao, Ximing Zhang, Chenji Lu +5
Effective query-item relevance modeling is pivotal for enhancing user experience and safeguarding user satisfaction in e-commerce search systems. Recently, benefiting from the vast…
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
Xiaoqiang Gui, Yueyao Cheng, Xiang-Rong Sheng +6
In machine learning systems, privileged features refer to the features that are available during offline training but inaccessible for online serving. Previous studies have recogni…
Robust Representation Learning for Unified Online Top-K Recommendation
Minfang Lu, Yuchen Jiang, Huihui Dong +9
In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse bus…