Over the years, the recommender systems (RS) have witnessed an increasing growth for its
enormous benefits in supporting users' needs through mapping the available products to …

The recommender system (RS) filters out important information from a large pool of
dynamically generated information to set some important decisions in terms of some …

With the huge amount of information available on the Internet, recommendation systems
gained popularity over the years. Traditional recommendation algorithm usually uses …

Recommender systems use information filtering to predict user preferences. They are
becoming a vital part of e-business and are used in a wide variety of industries, ranging from …

eWOM (electronic word-of-mouth) communities not only help their users to gain insights
through the exchange of information about products, but also to make the right purchase …

Recommendation engines are a subclass of information filtering system that seeks to predict
the 'rating'or 'preference'that user would give to an item. It finds information designs in the …

Data sparsity remains to be a critical concern for recommendation systems since it results in
low accuracy and poor recommendation quality. To address this problem, collaborative …

The Recommender System (RS) has grown exponentially in the last several years in many
different groups. Researchers are interested in it since many companies operating on online …

Personalized recommendation technology is a valid way to solve the problem of “information
overload”. In the face the complexity of agricultural field information and problems of farmers' …

E-Commerce or Online trading platforms work on the idea of Recommendation Engines
[REs], where products/items are provided or recommended as per the end user's/customer's …