Cold Start problems in recommender systems pose various challenges in the adoption and
use of recommender systems, especially for new item uptake and new user engagement …

Recommender Systems are severely hampered by the well-known Cold Start problem,
identified by the lack of information on new items and users. This has led to research efforts …

Heterogeneous information networks are increasingly used in recommendation algorithms.
However, they lack an explicit representation of meta-paths. In using bidirectional neural …

To the best of our knowledge, few studies have focused on the inconsistency between user
ratings and reviews as well as natural noise management in recommender systems (RSs) …

Purpose: Purpose of this research is to carry out survey on Recommendation systems
techniques in Big Data Analytics. This article presents designing of recommender systems …

Recommender systems (RSs) have become increasingly vital in the modern information era
and connected economy. They play a key role in business operations by generating …

Collaborative filtering (CF) is an important approach for recommendation system which is
widely used in a great number of aspects of our life, heavily in the online-based commercial …

With the popularization of the Internet, various online learning platforms have developed
rapidly, providing users with abundant learning resources, and realizing personalized …

Purpose: Purpose of this research is to carry out survey on Recommendation systems
techniques in Big Data Analytics. This article presents designing of recommender systems …

With the popularity of smartphones, many people use mobile phones to provide
personalised recommendations in a smart city. Aggregate diversity is defined as …