1 citations · 1 across the 13 of their papers we have counts for
14 papers
RecGPT-Mobile-V2 Technical Report
Lingqing Zhang, Bin Zhang, Weipeng Huang +25
Personalized Query prediction maps implicit behavioral signals---clicks, favorites, purchases, and post-purchase exploration---to explicit retrieval intent. On-device deployment ma…
SSRLive: Live Streaming Recommendation with Dynamic Semantic ID
Teng Shi, Zhaoheng Li, Yuanhang Qu +3
Live streaming has emerged as one of the fastest-growing forms of online media, enabling instant content broadcasting and real-time engagement between users and streamers. Despite…
RecGPT-Mobile: On-Device Large Language Models for User Intent Understanding in Taobao Feed Recommendation
Bin Zhang, Weipeng Huang, Dimin Wang +9
Predicting a user's next search query from recent interaction behaviors is a critical problem in modern e-commerce systems, particularly in scenarios where user intent evolves rapi…
A Long-term Value Prediction Framework In Video Ranking
Huabin Chen, Xinao Wang, Huiping Chu +5
Accurately modeling long-term value (LTV) at the ranking stage of short-video recommendation remains challenging. While delayed feedback and extended engagement have been explored,…
HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders
Kun Yuan, Junyu Bi, Daixuan Cheng +5
Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory…
Generative Pseudo-Labeling for Pre-Ranking with LLMs
Junyu Bi, Xinting Niu, Daixuan Cheng +4
Pre-ranking is a critical stage in industrial recommendation systems, tasked with efficiently scoring thousands of recalled items for downstream ranking. A key challenge is the tra…
CoNRec: Context-Discerning Negative Recommendation with LLMs
Xinda Chen, Jiawei Wu, Yishuang Liu +5
Understanding what users like is relatively straightforward; understanding what users dislike, however, remains a challenging and underexplored problem. Research into users' negati…
ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users
Dakai Zhai, Jiong Gao, Boya Du +4
Accurately predicting conversion rates (CVR) for low-activity users remains a fundamental challenge in large-scale e-commerce recommender systems. Existing approaches face three cr…
SaviorRec: Semantic-Behavior Alignment for Cold-Start Recommendation
Yining Yao, Ziwei Li, Shuwen Xiao +5
In recommendation systems, predicting Click-Through Rate (CTR) is crucial for accurately matching users with items. To improve recommendation performance for cold-start and long-ta…
RecGPT Technical Report
Chao Yi, Dian Chen, Gaoyang Guo +51
Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, mo…
AliBoost: Ecological Boosting Framework in Alibaba Platform
Qijie Shen, Yuanchen Bei, Zihong Huang +8
Maintaining a healthy ecosystem in billion-scale online platforms is challenging, as users naturally gravitate toward popular items, leaving cold and less-explored items behind. Th…
Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation
Jiakai Tang, Sunhao Dai, Teng Shi +5
Sequential Recommendation (SeqRec) aims to predict the next item by capturing sequential patterns from users' historical interactions, playing a crucial role in many real-world rec…
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
Yunjia Xi, Muyan Weng, Wen Chen +9
Recommender systems (RSs) often suffer from the feedback loop phenomenon, e.g., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that…
Simple but Efficient: A Multi-Scenario Nearline Retrieval Framework for Recommendation on Taobao
Yingcai Ma, Ziyang Wang, Yuliang Yan +5
In recommendation systems, the matching stage is becoming increasingly critical, serving as the upper limit for the entire recommendation process. Recently, some studies have start…