Recommender Systems are currently highly relevant for helping users deal with the
information overload they suffer from the large volume of data on the web, and automatically …

Precise user and item embedding learning is the key to building a successful recommender
system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item …

Background Health recommender systems (HRSs) are information retrieval systems that
provide users with relevant items according to the users' needs, which can motivate and …

With the advent and popularity of social network, more and more users like to share their
experiences, such as ratings, reviews, and blogs. The new factors of social network like …

From social media has emerged continuous needs for automatic travel recommendations.
Collaborative filtering (CF) is the most well-known approach. However, existing approaches …

Exponential growth of information generated by online social networks demands effective
recommender systems to give useful results. Traditional techniques become unqualified …

User modeling aims to capture the latent characteristics of users from their behaviors, and is
widely applied in numerous applications. Usually, centralized user modeling suffers from the …

A recommendation system is an integral part of any modern online shopping or social
network platform. The product recommendation system as a typical example of the legacy …

Topic modelling is a powerful text mining tool that has been applied in many fields such as
software engineering, political and linguistic sciences. To evaluate the development of topic …

Exponential growth of information generated by online social networks demands effective
and scalable recommender systems to give useful results. Traditional techniques become …