The integration of artificial intelligence (AI) into daily life, particularly through information
retrieval and recommender systems, has necessitated advanced user modeling and …

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

Given a user, a personalized search model relies on her historical behaviors, such as issued
queries and their clicked documents, to generate an interest profile and personalize search …

Personalization of search results has gained increasing attention in the past few years, also
thanks to the development of Neural Networks-based approaches for Information Retrieval …

Personalized search and search result diversification are two possible solutions to cope with
the query ambiguity problem in search engines. In most existing studies, they have been …

In the last decades, user profiles have been used in several areas of information technology.
In the literature, most research works, and systems focus on the creation of profiles (using …

As the last stage of recommender systems, re-ranking generates a re-ordered list that aligns
with the user's preference. However, previous works generally focus on item-level positive …

Personalized search plays an important role in satisfying users' information needs owing to
its ability to build user profiles based on users' search histories. Most of the existing …

The personalization of search results has gained increasing attention in the past few years,
thanks to the development of Neural Networks-based approaches for Information Retrieval …

To accurately understand user information needs and provide better search experiences,
various methods have been proposed to model user search intent from their search logs …