Recommender systems have significantly developed in recent years in parallel with the
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …

An essential goal of recommendation systems is to provide users with accurate and
personalized recommendations that meet their preferences. With the rapid growth of IoT …

Recommender systems use intelligent algorithms to learn a user's preferences and provide
them relevant suggestions. Lack of sufficient ratings–also known as data sparsity problem …

Green finance can be referred to as financial investments made on sustainable projects and
policies that focus on a sustainable economy. The procedures include promoting renewable …

Context-aware recommender systems dedicated to online social networks experienced
noticeable growth in the last few years. This has led to more research being done in this …

Recommender systems are software mechanisms whose usage is to offer suggestions for
different types of entities like products, services, or contacts that could be useful or …

A context-aware recommender system (CARS) utilizes users' context to provide
personalized services. Contextual information can be derived from sensors in order to …

Recommender systems can assist with decision-making by delivering a list of item
recommendations tailored to user preferences. Context-aware recommender systems …

Context-aware collaborative filtering is an efficient method for tailoring recommendations to
the individual contextual settings of users, with the objective of improving prediction …

Context plays an important role in the process of decision making. A user's preferences on
the items may vary from contexts to contexts, eg, a user may prefer to watch a different type …