11 citations · 22 across the 13 of their papers we have counts for
15 papers
Inherit4Rec: Parameter Inheritance for Efficient Scaling of Recommendation Models
Ruihao Zhang, Bo Chen, Xiao Wang +8
Scaling model capacity has emerged as an effective approach to overcoming performance bottlenecks in industrial recommender systems. However, repeatedly training larger dense model…
SITA: Semantic Interest Tokens for Target-Aware Compression in Long-Sequence Recommendation
Rui Zhou, Bo Chen, Qinglin Jia +5
As user behavior histories continue to grow on modern Internet platforms, effectively modeling long behavior sequences has become crucial for predicting user interests in candidate…
UniFormer: Efficient and Unified Model-Centric Scaling for Industrial Recommendation
Bo Chen, Jinlong Jiao, Tijian Hu +12
Recently, substantial progress has been made in industrial recommendation through component-centric model scaling, where individual components such as behavior modeling, feature in…
DIET: Learning to Distill Dataset Continually for Recommender Systems
Jiaqing Zhang, Hao Wang, Mingjia Yin +6
Modern deep recommender models are trained under a continual learning paradigm, relying on massive and continuously growing streaming behavioral logs. In large-scale platforms, ret…
FairFS: Addressing Deep Feature Selection Biases for Recommender System
Xianquan Wang, Zhaocheng Du, Jieming Zhu +3
Large-scale online marketplaces and recommender systems serve as critical technological support for e-commerce development. In industrial recommender systems, features play vital r…
No One Left Behind: How to Exploit the Incomplete and Skewed Multi-Label Data for Conversion Rate Prediction
Qinglin Jia, Zhaocheng Du, Chuhan Wu +4
In most real-world online advertising systems, advertisers typically have diverse customer acquisition goals. A common solution is to use multi-task learning (MTL) to train a unifi…
TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems
Xianquan Wang, Zhaocheng Du, Jieming Zhu +3
Feature interaction modeling is crucial for deep recommendation models. A common and effective approach is to construct explicit feature combinations to enhance model performance.…
CHOP: Mobile Operating Assistant with Constrained High-frequency Optimized Subtask Planning
Yuqi Zhou, Shuai Wang, Sunhao Dai +4
The advancement of visual language models (VLMs) has enhanced mobile device operations, allowing simulated human-like actions to address user requirements. Current VLM-based mobile…
Few-shot LLM Synthetic Data with Distribution Matching
Jiyuan Ren, Zhaocheng Du, Zhihao Wen +4
As large language models (LLMs) advance, their ability to perform in-context learning and few-shot language generation has improved significantly. This has spurred using LLMs to pr…
Evaluating Conversational Recommender Systems via Large Language Models: A User-Centric Framework
Nuo Chen, Quanyu Dai, Xiaoyu Dong +5
Conversational recommender systems (CRSs) integrate both recommendation and dialogue tasks, making their evaluation uniquely challenging. Existing approaches primarily assess CRS p…
RecSys Arena: Pair-wise Recommender System Evaluation with Large Language Models
Zhuo Wu, Qinglin Jia, Chuhan Wu +4
Evaluating the quality of recommender systems is critical for algorithm design and optimization. Most evaluation methods are computed based on offline metrics for quick algorithm e…
Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation
Yuang Zhao, Zhaocheng Du, Qinglin Jia +3
With the increase in the business scale and number of domains in online advertising, multi-domain ad recommendation has become a mainstream solution in the industry. The core of mu…
Confidence-Aware Multi-Field Model Calibration
Yuang Zhao, Chuhan Wu, Qinglin Jia +6
Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding. However, there often exist unwanted mis…
Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
Yifan Wang, Peijie Sun, Min Zhang +3
Conversion rate prediction is critical to many online applications such as digital display advertising. To capture dynamic data distribution, industrial systems often require retra…
Contrastive Multi-view Framework for Customer Lifetime Value Prediction
Chuhan Wu, Jingjie Li, Qinglin Jia +3
Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications. However, the heavy sparsity of cons…