Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

In this paper, we introduce an embedding model, named CapsE, exploring a capsule
network to model relationship triples (subject, relation, object). Our CapsE represents each …

How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

Explainable information retrieval is an emerging research area aiming to make transparent
and trustworthy information retrieval systems. Given the increasing use of complex machine …

Twitter, due to its massive growth as a social networking platform, has been in focus for the
analysis of its user generated content for personalization and recommendation tasks. A …

The recent rapid growth of the Internet content has led to building recommendation systems
that guide users to their needs through an information retrieving process. An expert …

Personalized search plays a crucial role in improving user search experience owing to its
ability to build user profiles based on historical behaviors. Previous studies have made great …

The key to personalized search is to clarify the meaning of current query based on user's
search history. Previous personalized studies tried to build user profiles on the basis of …

Search results personalization has become an effective way to improve the quality of search
engines. Previous studies extracted information such as past clicks, user topical interests …

Topic modeling has been widely applied in a variety of industrial applications. Training a
high-quality model usually requires massive amount of in-domain data, in order to provide …