1 citations · 1 across the 22 of their papers we have counts for
23 papers
SPARC: Sequence-aware Progressive Attribute Routing and Compression Framework for Generative Recommendation
Chang Liu, Changfa Wu, Hui Qian +5
Generative recommendation tokenizes items as discrete Semantic IDs (SIDs) and autoregressively generates target items from users' historical SID sequences. Although existing SIDs i…
TransRetrieval: Scaling Up Transformer-Based Retrieval for Industrial Recommendation
Zhifei Zheng, Yunfei Liu, Bin Liu +7
Applying scaling laws to recommendation retrieval is hindered by feature heterogeneity: naively stacking Transformer layers yields diminishing returns because heterogeneous fields…
Native Multimodal Representation Learning for Click-Through Rate Prediction in E-Commerce Scenarios
Chao Yi, Feifan Yang, Jiawei Feng +4
Multimodal representations have been widely adopted in industrial e-commerce recommendation systems. Due to their strong semantic understanding and generalization capabilities, the…
Towards Faithful Simulation of Human Shopping Behavior
Jiakai Tang, Yan Mi, Jing Yu +9
Simulating realistic user shopping behavior underpins offline evaluation and reinforcement learning in e-commerce scenarios. While recent LLM- and VLM-based simulators have made en…
Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?
Jinqi Wu, Sishuo Chen, Zhangming Chan +9
Touchpoint selection in conversion attribution, namely identifying meaningful touchpoints contributing to conversions, is essential for e-commerce recommendation and online adverti…
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…
Learning from the Future: Privileged Self-Distillation for Sequential Recommendation
Jiakai Tang, Yang Zhang, See-Kiong Ng +4
Sequential recommenders are commonly trained with one-hot next-item labels under a causal (prefix-only) objective aligned with inference. While deployment-compatible, this supervis…
LoopMemGR: From Behavior Logs to Evolving Memory for Generative Recommendation
Hui Qian, Changfa Wu, Chang Liu +6
Generative recommendation formulates next-item prediction as conditional autoregressive generation over discrete Semantic IDs, enabling end-to-end recommendation over large-scale i…
MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms
Jinqi Wu, Sishuo Chen, Zhangming Chan +9
Multi-attribution learning (MAL), which enhances model performance by learning from conversion labels yielded by multiple attribution mechanisms, has emerged as a promising learnin…
MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling
Bin Wu, Feifan Yang, Zhangming Chan +8
Lifelong user interest modeling is crucial for industrial recommender systems, yet existing approaches rely predominantly on ID-based features, suffering from poor generalization o…
AIF: Asynchronous Inference Framework for Cost-Effective Pre-Ranking
Zhi Kou, Xiang-Rong Sheng, Shuguang Han +5
In industrial recommendation systems, pre-ranking models based on deep neural networks (DNNs) commonly adopt a sequential execution framework: feature fetching and model forward co…
Think before Recommendation: Autonomous Reasoning-enhanced Recommender
Xiaoyu Kong, Junguang Jiang, Bin Liu +6
The core task of recommender systems is to learn user preferences from historical user-item interactions. With the rapid development of large language models (LLMs), recent researc…
See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction
Sishuo Chen, Zhangming Chan, Xiang-Rong Sheng +6
Conversion rate (CVR) prediction is a core component of online advertising systems, where the attribution mechanisms-rules for allocating conversion credit across user touchpoints-…
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…
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…
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…
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao
Jingyue Gao, Shuguang Han, Han Zhu +4
Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user f…
COPR: Consistency-Oriented Pre-Ranking for Online Advertising
Zhishan Zhao, Jingyue Gao, Yu Zhang +8
Cascading architecture has been widely adopted in large-scale advertising systems to balance efficiency and effectiveness. In this architecture, the pre-ranking model is expected t…
Adaptive Domain Interest Network for Multi-domain Recommendation
Yuchen Jiang, Qi Li, Han Zhu +5
Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously. In the ret…
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models
Jingwei Zhuo, Bin Liu, Xiang Li +2
Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates l…
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
Rihan Chen, Bin Liu, Han Zhu +8
Model-based methods for recommender systems have been studied extensively for years. Modern recommender systems usually resort to 1) representation learning models which define use…
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration
Siguang Huang, Yunli Wang, Lili Mou +4
Most machine learning classifiers only concern classification accuracy, while certain applications (such as medical diagnosis, meteorological forecasting, and computation advertisi…