23 papers
AdaM-Rec: Adaptive Modality Routing for Multimodal Recommendation
Honghao Fu, Jiacheng Chen, Manxi Lin +7
While recent multimodal recommender systems have demonstrated the effectiveness of incorporating visual and textual information to improve downstream performance, most existing met…
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
RecGPT-V3 Technical Report
Bowen Zheng, Chao Yi, Dian Chen +26
Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecG…
ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
Jiacheng Chen, Tao Zhang, Manxi Lin +26
The wave of AI-native applications is moving shopping beyond page- and feed-based browsing toward intent-driven experiences orchestrated by LLM agents. A common design wraps an LLM…
Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocation
Ge Fan, Nan Zhao, Kai Meng +6
With the rapid evolution of internet services, recommendation systems have become indispensable. In particular, the blending (re-ranking) stage plays a pivotal role in allocating t…
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction
Jiakai Tang, Runfeng Zhang, Weiqiu Wang +7
Scaling Transformer-based click-through rate (CTR) models by stacking more parameters brings growing computational and storage overhead, creating a widening gap between scaling amb…
SecAgent: Efficient Mobile GUI Agent with Semantic Context
Yiping Xie, Song Chen, Jingxuan Xing +7
Mobile Graphical User Interface (GUI) agents powered by multimodal large language models have demonstrated promising capabilities in automating complex smartphone tasks. However, e…
Multi-Behavior Sequential Modeling with Transition-Aware Graph Attention Network for E-Commerce Recommendation
Hanqi Jin, Gaoming Yang, Zhangming Chan +7
User interactions on e-commerce platforms are inherently diverse, involving behaviors such as clicking, favoriting, adding to cart, and purchasing. The transitions between these be…
Parallel Latent Reasoning for Sequential Recommendation
Jiakai Tang, Xu Chen, Wen Chen +3
Capturing complex user preferences from sparse behavioral sequences remains a fundamental challenge in sequential recommendation. Recent latent reasoning methods have shown promise…
AndroidLens: Long-latency Evaluation with Nested Sub-targets for Android GUI Agents
Yue Cao, Yingyao Wang, Pi Bu +10
Graphical user interface (GUI) agents can substantially improve productivity by automating frequently executed long-latency tasks on mobile devices. However, existing evaluation be…
RecGPT-V2 Technical Report
Chao Yi, Dian Chen, Gaoyang Guo +32
Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. Whil…
Beyond Existing Retrievals: Cross-Scenario Incremental Sample Learning Framework
Tao Wang, Xun Luo, Jinlong Guo +4
The parallelized multi-retrieval architecture has been widely adopted in large-scale recommender systems for its computational efficiency and comprehensive coverage of user interes…
Interactive Recommendation Agent with Active User Commands
Jiakai Tang, Yujie Luo, Xunke Xi +12
Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to captu…
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…
MIRAGE: Towards AI-Generated Image Detection in the Wild
Cheng Xia, Manxi Lin, Jiexiang Tan +6
The spreading of AI-generated images (AIGI), driven by advances in generative AI, poses a significant threat to information security and public trust. Existing AIGI detectors, whil…
InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning
Qihang Ai, Pi Bu, Yue Cao +8
Recent advances in Vision-Language Models (VLMs) have enabled mobile agents to perceive and interact with real-world mobile environments based on human instructions. However, the c…
TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios
Zida Liang, Changfa Wu, Dunxian Huang +9
Recommendation systems are essential tools in modern e-commerce, facilitating personalized user experiences by suggesting relevant products. Recent advancements in generative model…
Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training
Jihao Gu, Qihang Ai, Yingyao Wang +10
Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinforcement learning paradigms like G…
TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
Jiahao Yu, Haozhuang Liu, Yeqiu Yang +4
Regression models are crucial in recommender systems. However, retransformation bias problem has been conspicuously neglected within the community. While many works in other fields…
WiS Platform: Enhancing Evaluation of LLM-Based Multi-Agent Systems Through Game-Based Analysis
Chengwei Hu, Jianhui Zheng, Yancheng He +7
Recent advancements in autonomous multi-agent systems (MAS) based on large language models (LLMs) have enhanced the application scenarios and improved the capability of LLMs to han…
Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights
Xiang-Rong Sheng, Feifan Yang, Litong Gong +10
Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, pred…
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…