In the recent era, recommendation systems have marked their footsteps and
have changed the way of the travel industry. The recommendation system deals with
massive amounts of data to identify users’ interests, making the location search easier.
Many methods have been used so far for making predictions much more desirable
regarding users’ interests by collecting Information from a large set of other users. The
main objective of this paper is to show various methods and techniques used for
generating recommendations. These recommendation processes are classified into
different forms, such as traditional methods and tensor-based methods. A brief review
of these methods was described with the help of some challenges faced by the
recommendation system. Apart from that, the advantages and disadvantages are
discussed, along with the highlights of future directions.
Keywords: Point of Interest, Tensor factorization, Recommendation, Collaborative Filtering, Check-in, Tucker decomposition, Preference.
