16 citations · 52 across the 20 of their papers we have counts for
10 papers · 1 filter
Multi-Scenario Ranking with Adaptive Feature Learning
Yu Tian, Bofang Li, Si Chen +6
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mi…
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach
Zhangming Chan, Yu Zhang, Shuguang Han +8
Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR e…
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
Xiang-Rong Sheng, Jingyue Gao, Yueyao Cheng +6
Despite the development of ranking optimization techniques, pointwise loss remains the dominating approach for click-through rate prediction. It can be attributed to the calibratio…
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
Xuanhua Yang, Xiaoyu Peng, Penghui Wei +3
Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple…
Visual Encoding and Debiasing for CTR Prediction
Si Chen, Chen Lin, Wanxian Guan +7
Extracting expressive visual features is crucial for accurate Click-Through-Rate (CTR) prediction in visual search advertising systems. Current commercial systems use off-the-shelf…
AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System
Zhirong Xu, Shiyang Wen, Junshan Wang +8
Graph embedding based retrieval has become one of the most popular techniques in the information retrieval community and search engine industry. The classical paradigm mainly relie…