Xiaoqiang Zhu

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

2019

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have …

Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

2019

Click-through rate (CTR) prediction is critical for industrial applications such as recommender system and online advertising. Practically, it plays an important role for CTR modeling in these applications by mining user interest from rich historical …

Deep Interest Evolution Network for Click-Through Rate Prediction

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Click-through rate (CTR) prediction, whose goal is to estimate the probability of a user clicking on the item, has become one of the core tasks in the advertising system. For CTR prediction model, it is …

Entire Space Multi-Task Model

2018

Estimating post-click conversion rate (CVR) accurately is crucial for ranking systems in industrial applications such as recommendation and advertising. Conventional CVR modeling applies popular deep learning methods and achieves state-of-the-art performance. However it encounters several …

Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction

2020

Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online advertising. Both industry and academy have paid much …

One Model to Serve All

2021

Traditional industry recommendation systems usually use data in a single domain to train models and then serve the domain. However, a large-scale commercial platform often contains multiple domains, and its recommendation system often needs to …