With the growth of online information, varying personalization drifts and volatile behaviors of
internet users, recommender systems are effective tools for information filtering to overcome …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

In this paper, we introduce a large scale multi-objective ranking system for recommending
what video to watch next on an industrial video sharing platform. The system faces many …

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building
recommender systems in industrial settings, owing to its interpretability and efficiency in real …

Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …

Recommender systems are feedback loop systems, which often face bias problems such as
popularity bias, previous model bias and position bias. In this paper, we focus on solving the …

Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems
in industry, owing to its strength in user interest modeling and ease in online …

Recommender systems deployed in real-world applications can have inherent exposure
bias, which leads to the biased logged data plaguing the researchers. A fundamental way to …

Recommendation systems play a vital role to keep users engaged with personalized
contents in modern online platforms. Recently, deep learning has revolutionized many …

User experience in modern content discovery applications critically depends on high-quality
personalized recommendations. However, building systems that provide such …