15 citations · 22 across the 24 of their papers we have counts for
15 papers · 1 filter
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…
NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative Recommendation (GR), powered by Large Language Models (LLMs), represents a promising new paradigm for industrial recommender systems. However, their practical applicatio…
MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understanding
Zhanheng Nie, Chenghan Fu, Daoze Zhang +5
Recent Multimodal Large Language Models (MLLMs) have significantly advanced e-commerce product understanding. However, they still face three challenges: (i) the modality imbalance…
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…
Creative4U: MLLMs-based Advertising Creative Image Selector with Comparative Reasoning
Yukang Lin, Xiang Zhang, Shichang Jia +9
Creative image in advertising is the heart and soul of e-commerce platform. An eye-catching creative image can enhance the shopping experience for users, boosting income for advert…
MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding
Daoze Zhang, Chenghan Fu, Zhanheng Nie +7
With the rapid advancement of e-commerce, exploring general representations rather than task-specific ones has attracted increasing research attention. For product understanding, a…
Beyond Advertising: Mechanism Design for Platform-Wide Marketing Service "QuanZhanTui"
Ningyuan Li, Zhilin Zhang, Tianyan Long +8
On e-commerce platforms, sellers typically bid for impressions from ad traffic to promote their products. However, for most sellers, the majority of their sales come from organic t…
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…
Gradient Deconfliction via Orthogonal Projections onto Subspaces For Multi-task Learning
Shijie Zhu, Hui Zhao, Tianshu Wu +4
Although multi-task learning (MTL) has been a preferred approach and successfully applied in many real-world scenarios, MTL models are not guaranteed to outperform single-task mode…
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…