15 citations · 22 across the 24 of their papers we have counts for
14 papers · 1 filter
LORE: A Large Generative Model for Search Relevance
Chenji Lu, Zhuo Chen, Hui Zhao +9
Achievement. We introduce LORE, a systematic framework for Large Generative Model-based relevance in e-commerce search. Deployed and iterated over three years, LORE achieves a cumu…
RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment
Chenji Lu, Zhuo Chen, Hui Zhao +4
Search relevance plays a central role in web e-commerce. While large language models (LLMs) have shown significant results on relevance task, existing benchmarks lack sufficient co…
MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising
Chenghan Fu, Daoze Zhang, Yukang Lin +8
We introduce MOON, our comprehensive set of sustainable iterative practices for multimodal representation learning for e-commerce applications. MOON has already been fully deployed…
From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction
Bencheng Yan, Yuejie Lei, Zhiyuan Zeng +7
Despite massive investments in scale, deep models for click-through rate (CTR) prediction often exhibit rapidly diminishing returns -- a stark contrast to the {predictable scaling…
RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou +31
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training fra…
GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scen…
VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search
Xiao Zhang, Guanyu Chen, Boyang Zuo +4
Query-to-bidword(i.e., bidding keyword) rewriting is fundamental to sponsored search, transforming noisy user queries into semantically relevant and commercially valuable keywords.…
UQABench: Evaluating User Embedding for Prompting LLMs in Personalized Question Answering
Langming Liu, Shilei Liu, Yujin Yuan +10
Large language models (LLMs) achieve remarkable success in natural language processing (NLP). In practical scenarios like recommendations, as users increasingly seek personalized e…
MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling
Bencheng Yan, Si Chen, Shichang Jia +12
Click-Through Rate (CTR) prediction is a crucial task in recommendation systems, online searches, and advertising platforms, where accurately capturing users' real interests in con…
Unlocking Scaling Law in Industrial Recommendation Systems with a Three-step Paradigm based Large User Model
Bencheng Yan, Shilei Liu, Zhiyuan Zeng +10
Recent advancements in autoregressive Large Language Models (LLMs) have achieved significant milestones, largely attributed to their scalability, often referred to as the "scaling…
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…
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction
Bencheng Yan, Pengjie Wang, Kai Zhang +4
In many web applications, deep learning-based CTR prediction models (deep CTR models for short) are widely adopted. Traditional deep CTR models learn patterns in a static manner, i…
Binary Code based Hash Embedding for Web-scale Applications
Bencheng Yan, Pengjie Wang, Jinquan Liu +4
Nowadays, deep learning models are widely adopted in web-scale applications such as recommender systems, and online advertising. In these applications, embedding learning of catego…
Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach
Xu Ma, Pengjie Wang, Hui Zhao +6
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted. Such architecture usually consists of matching, pre-ranking…