7 papers
NOVA: A Verification-Aware Agent Harness for Architecture Evolution in Industrial Recommender Systems
Shaohua Liu, Liang Fang, Yilong Sun +16
Industrial advertising recommender systems are continually improved through architecture modifications, yet production iteration remains expert-intensive because coordinated change…
SIREN: Unified Multi-Granularity Semantic Interaction for Multi-Modal Lifelong User Interest Modeling
Yaqian Zhang, Ruyi Yu, Tianyi Li +13
Industrial recommender systems increasingly leverage lifelong user behavior histories and rich multi-modal content to capture evolving user preferences. However, effectively integr…
FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
Zenan Dai, Jinpeng Wang, Junwei Pan +3
Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-domain behavioral data. While fre…
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System
Fengxin Li, Yi Li, Yue Liu +11
Display advertising provides significant value to advertisers, publishers, and users. Traditional display advertising systems utilize a multi-stage architecture consisting of retri…
Crocodile: Cross Experts Covariance for Disentangled Learning in Multi-Domain Recommendation
Zhutian Lin, Junwei Pan, Haibin Yu +7
Multi-domain learning (MDL) has become a prominent topic in enhancing the quality of personalized services. It's critical to learn commonalities between domains and preserve the di…
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
Zhutian Lin, Junwei Pan, Shangyu Zhang +5
Click-through rate (CTR) prediction is a crucial area of research in online advertising. While binary cross entropy (BCE) has been widely used as the optimization objective for tre…