Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

In modern commercial search engines and recommendation systems, data from multiple
domains is available to jointly train the multi-domain model. Traditional methods train multi …

Reciprocal Recommender Systems (RRSs) which aim to satisfy the preferences of both
service providers and seekers simultaneously has attracted significant research interest in …

Search and recommendation (S&R) are the two most important scenarios in e-commerce.
The majority of users typically interact with products in S&R scenarios, indicating the need …

In this work, we propose a Unified framework of Sequential Search and Recommendation
(UnifiedSSR) for joint learning of user behavior history in both search and recommendation …

The confluence of Search and Recommendation (S&R) services is vital to online services,
including e-commerce and video platforms. The integration of S&R modeling is a highly …

In e-commerce recommendation systems, users' long-term and short-term interests jointly
influence product selection. However, the behavioral conformity phenomenon tends to be …

In recommender systems, various latent confounding factors (eg, user social environment
and item public attractiveness) can affect user behavior, item exposure, and feedback in …

The modeling of users' behaviors is crucial in modern recommendation systems. A lot of
research focuses on modeling users' lifelong sequences, which can be extremely long and …

Nowadays, many platforms provide users with both search and recommendation services as
important tools for accessing information. The phenomenon has led to a correlation between …