Research Area:  Machine Learning

Abstract:

Recommendation systems are gaining increasing popularity in many application areas like e-commerce, movie and music recommendations, tourism, news, advertisement, stock markets, social networks etc. Conventional recommendation systems either use content based or collaborative filtering based approaches to model user preferences and give recommendations. These systems usually fail to consider evolving user preferences in different contextual situations. Context Aware Recommendation Systems take different contextual attributes into consideration and try to capture user preferences correctly. This survey focuses on the state-of-the art computational intelligence techniques trying to improve conventional design using contextual information. Further, these techniques are grouped into bio-inspired computing techniques and statistical computing techniques. The literature related to these techniques mentioning their ability to handle challenges faced by Context Aware Recommendation System are presented in this survey. The survey also talks about context inclusion strategies, classification of the contexts used in the literature reviewed, their impact on the problems faced by the recommendation systems, effective usage of these contexts, datasets used in the domain, future research scope in all the reviewed techniques and overall future research directions and challenges.

Keywords:  

Author(s) Name:  Saurabh Kulkarni, Sunil F.Rodd

Publisher name:  Elsevier

DOI:  10.1016/j.cosrev.2020.100255

Volume Information:  Volume 37, August 2020, 100255

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S1574013719301406