Most of the existing recommender systems understand the preference level of users based
on user-item interaction ratings. Rating-based recommendation systems mostly ignore …

Recommender Systems focus on implicit and explicit feedback or parameters of users for
better rating prediction. Most of the existing recommender systems use only one type of …

E-commerce companies want to predict their future product sales from the current customers'
feedback to frame a better business strategy. However, the conventional way of analyzing …

In the current research, Graph Neural Networks (GNNs) play a decisive role in learning from
network data structure. In a social recommender system, GNNs have a significant …

Recommender systems (RSs) have emerged as very useful tools to help customers with
their decision-making process, find items of their interest, and alleviate the information …

Recommendation systems usually make a personalized recommendation with explicit
feedback (ie, ratings, reviews, and description on products) or implicit feedback (ie …

Traditional Collaborative Filtering (CF) based methods are applied to understand the
personal preferences of users/customers for items or products from the rating matrix …

Recommender systems are a popular solution for the problem of information overload,
offering personalized recommendations to users. Recent years, research has aimed to …

Session-based recommendation (SBR) is to predict the items that users are likely to click
afterward by using their recent click history. Learning item features from existing session …

Helping users to find pertinent documents within a big corpus through the use of simple
queries on a search engine is a major concern in the information retrieval field. The work …