59 citations · 112 across the 13 of their papers we have counts for
13 papers
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search
Longbin Li, Chao Zhang, Sen Li +3
In e-commerce search, personalized retrieval is a crucial technique for improving user shopping experience. Recent works in this domain have achieved significant improvements by th…
Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System
Zhixuan Zhang, Yuheng Huang, Dan Ou +4
E-commerce search systems such as Taobao Search, the largest e-commerce searching system in China, aim at providing users with the most preferred items (e.g., products). Due to the…
Cross-domain Augmentation Networks for Click-Through Rate Prediction
Xu Chen, Zida Cheng, Shuai Xiao +2
Data sparsity is an important issue for click-through rate (CTR) prediction, particularly when user-item interactions is too sparse to learn a reliable model. Recently, many works…
Category-Oriented Representation Learning for Image to Multi-Modal Retrieval
Zida Cheng, Chen Ju, Shuai Xiao +5
The rise of multi-modal search requests from users has highlighted the importance of multi-modal retrieval (i.e. image-to-text or text-to-image retrieval), yet the more complex tas…
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training
Xiaoyang Zheng, Fuyu Lv, Zilong Wang +2
E-commerce search engines comprise a retrieval phase and a ranking phase, where the first one returns a candidate product set given user queries. Recently, vision-language pre-trai…
MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search
Xiaoyang Zheng, Zilong Wang, Ke Xu +4
Taobao Search consists of two phases: the retrieval phase and the ranking phase. Given a user query, the retrieval phase returns a subset of candidate products for the following ra…
Multi-Objective Personalized Product Retrieval in Taobao Search
Yukun Zheng, Jiang Bian, Guanghao Meng +7
In large-scale e-commerce platforms like Taobao, it is a big challenge to retrieve products that satisfy users from billions of candidates. This has been a common concern of academ…
Intelligent Request Strategy Design in Recommender System
Xufeng Qian, Yue Xu, Fuyu Lv +6
Waterfall Recommender System (RS), a popular form of RS in mobile applications, is a stream of recommended items consisting of successive pages that can be browsed by scrolling. In…
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
Zhifang Fan, Dan Ou, Yulong Gu +7
Modeling user's historical feedback is essential for Click-Through Rate Prediction in personalized search and recommendation. Existing methods usually only model users' positive fe…
IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search
Dian Cheng, Jiawei Chen, Wenjun Peng +5
A good personalized product search (PPS) system should not only focus on retrieving relevant products, but also consider user personalized preference. Recent work on PPS mainly ado…
Embedding-based Product Retrieval in Taobao Search
Sen Li, Fuyu Lv, Taiwei Jin +5
Nowadays, the product search service of e-commerce platforms has become a vital shopping channel in people's life. The retrieval phase of products determines the search system's qu…
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
Jia-Qi Yang, Xiang Li, Shuguang Han +4
Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner t…
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
Xiang Li, Chao Wang, Jiwei Tan +3
For better user experience and business effectiveness, Click-Through Rate (CTR) prediction has been one of the most important tasks in E-commerce. Although extensive CTR prediction…