A commercially viable multi-stakeholder recommendation system maximizes the utility gain
by learning the personalized preferences of multiple stakeholders, such as consumers and …

In designing modern recommender systems, item feature information (or side information) is
often ignored as most models focus on exploiting rating information. However, the side …

Serendipity is a critical factor in the Recommender Systems (RS) in delivering pleasantly
surprising, novel, yet contextually relevant recommendations. Most existing methods …

Recommender Systems are helpful to many by filtering the information according to an
individual's preferences. However, the choice of a person may change with time. Keeping …

Numerous research efforts are endeavoring to boost the performance of dynamic user
preferences and next-item recommendations, which are pivotal tasks within sequential …

As recommender systems shift from rating-based to interaction-based models, graph neural
network-based collaborative filtering models are gaining popularity due to their powerful …

Sequential recommendation algorithm can predict the next action of a user by modeling the
user's interaction sequence with an item. However, most sequential recommendation …

Purpose The Multi-Stakeholder Recommendation System learns consumer and producer
preferences to make fair and balanced recommendations. Exclusive consumer-focused …

The commercially applicable Recommendation system (RS) exploits multi-criteria rating-
based user-item interaction to learn and personalize user preferences using the Multi …

Knowledge graph (KG) is increasingly important in improving recommendation performance
and handling item cold-start. A recent research hotspot is designing end-to-end models …