Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

Recently, deep learning based techniques exploiting reviews are extensively studied for
rating prediction and result in good performance. Some studies consider word level review …

Conversational recommender systems (CRS) aim to recommend high-quality items to users
through interactive conversations. Although several efforts have been made for CRS, two …

Recommender systems usually make personalized recommendation with user-item
interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the …

A large amount of information exists in reviews written by users. This source of information
has been ignored by most of the current recommender systems while it can potentially …

Numerous number of tourism attractions along with a huge amount of information about
them on web and social platforms have made the decision-making process for selecting and …

Recently, many e-commerce websites have encouraged their users to rate shop** items
and write review texts. This review information has been very useful for understanding user …

Modern recommender systems model people and items by discovering orteasing apart'the
underlying dimensions that encode the properties of items and users' preferences toward …

Although latent factor models (eg, matrix factorization) achieve good accuracy in rating
prediction, they suffer from several problems including cold-start, non-transparency, and …

The Web has accumulated a rich source of information, such as text, image, rating, etc,
which represent different aspects of user preferences. However, the heterogeneous nature …