5 citations · 11 across the 10 of their papers we have counts for
5 papers · 1 filter
AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising
Yang Yang, Bo Chen, Chenxu Zhu +6
Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficultie…
Prompt Tuning as User Inherent Profile Inference Machine
Yusheng Lu, Zhaocheng Du, Xiangyang Li +9
Large Language Models (LLMs) have exhibited significant promise in recommender systems by empowering user profiles with their extensive world knowledge and superior reasoning capab…
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…
CELA: Cost-Efficient Language Model Alignment for CTR Prediction
Xingmei Wang, Weiwen Liu, Xiaolong Chen +8
Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. The prevailing ID-based paradigm underperforms in cold-start scenarios due to the skewed dist…
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
Junjie Huang, Guohao Cai, Jieming Zhu +4
Click-through rate (CTR) prediction plays an indispensable role in online platforms. Numerous models have been proposed to capture users' shifting preferences by leveraging user be…