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 …

Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual
users and user groups by taking account of additional information about users besides their …

Many recommenders aim to provide relevant recommendations to users by building
personal topic interest profiles and then using these profiles to find interesting contents for …

Large language models (LLMs) have shown impressive capabilities in natural language
understanding and generation. Their potential for deeper user understanding and improved …

We present a context-aware neural ranking model to exploit users' on-task search activities
and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural …

Traditional search engines usually provide identical search results for all users, overlooking
individual preferences. To counter this limitation, personalized search has been developed …

Advancing the Search Frontier with AI Agents | Communications of the ACM skip to main
content ACM Digital Library home ACM Association for Computing Machinery corporate …

User intent modeling in natural language processing deciphers user requests to allow for
personalized responses. The substantial volume of research (exceeding 13,000 …

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 unprecedented growth of the Internet, its pervasive accessibility, and ease of use have
increased students' dependencies on the Web for quick search and retrieval of learning …