Recommender systems are firmly established as a standard technology for assisting users
with their choices; however, little attention has been paid to the application of the user model …

Hybrid recommender systems---systems using multiple algorithms together to improve
recommendation quality---have been well-known for many years and have shown good …

With the availability of inexpensive devices like storage and data sensors, collecting and
storing data is now simpler than ever. Biotechnology, pharmacy, business, online marketing …

In most of the recommendation systems, user rating is an important user activity that reflects
their opinions. Once the users return their ratings about items the systems have suggested …

Optimizing multiple objectives simultaneously is an important task for recommendation
platforms to improve their performance. However, this task is particularly challenging since …

Contextual information plays a key role in Context-Aware Recommender Systems (CARS).
The rating prediction in CARS focuses on improving recommendation accuracy attempting …

Recommender systems are routinely equipped with standardized taxonomy that associates
each item with one or more categories or genres. Although such information does not …

Channelizing product sales with the aid of Recommender Systems is ubiquitous in e-
commerce firms. Recommender systems help consumers by reducing their search cost by …

This study focuses on developing the multicriteria collaborative filtering algorithm for
improving the prediction accuracy. The approaches applied were user-item multirating …

Recommender Systems (RSs) cope with the problem of information overload, by providing
to users content that fit with what they prefer. Generally, RSs work much better for those …