Point of Interest Recommendation via Tensor Factorization

Abstract

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.<br>