Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual
improvement among tasks considering their shared knowledge. It is an important topic in …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Online advertising, as a vast market, has gained significant attention in various platforms
ranging from search engines, third-party websites, social media, and mobile apps. The …

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 …

Recommendation is a prevalent and critical service in information systems. To provide
personalized suggestions to users, industry players embrace machine learning, more …

Accurate estimation of post-click conversion rate is critical for building recommender
systems, which has long been confronted with sample selection bias and data sparsity …

Propensity-based weighting methods have been widely studied and demonstrated
competitive performance in debiased recommendations. Nevertheless, there are still many …

In most real-world large-scale online applications (eg, e-commerce or finance), customer
acquisition is usually a multi-step conversion process of audiences. For example, an …

The travel marketing platform of Alibaba serves an indispensable role for hundreds of
different travel scenarios from Fliggy, Taobao, Alipay apps, etc. To provide personalized …

Post-click conversion, as a strong signal indicating the user preference, is salutary for
building recommender systems. However, accurately estimating the post-click conversion …