2 citations · 5 across the 13 of their papers we have counts for
8 papers · 1 filter
ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation
Yihua Zhang, Mingfu Liang, Jiyan Yang +11
Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty.…
Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
Xiao Lin, Zhicheng Tang, Weilin Cong +14
Sequential recommendation has rapidly advanced in click-through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long…
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
Mingfu Liang, Xi Liu, Rong Jin +104
Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommenda…
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit
Huixue Zhou, Hengrui Gu, Xi Liu +15
The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance between computational efficiency and…
A Collaborative Ensemble Framework for CTR Prediction
Xiaolong Liu, Zhichen Zeng, Xiaoyi Liu +13
Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into…
InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
Zhichen Zeng, Xiaolong Liu, Mengyue Hang +25
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous informati…
AutoML for Large Capacity Modeling of Meta's Ranking Systems
Hang Yin, Kuang-Hung Liu, Mengying Sun +16
Web-scale ranking systems at Meta serving billions of users is complex. Improving ranking models is essential but engineering heavy. Automated Machine Learning (AutoML) can release…
DistDNAS: Search Efficient Feature Interactions within 2 Hours
Tunhou Zhang, Wei Wen, Igor Fedorov +8
Search efficiency and serving efficiency are two major axes in building feature interactions and expediting the model development process in recommender systems. On large-scale ben…