Influences of Deep Learning on Recommendation Systems

Deep structured learning is a noteworthy advancement in the subset of machine learning. Recommendation not only requires domain knowledge but also needs data science intuition to cope up with the abundance and complexity of data in the age of information overload. The plethora of information available on internet and especially on social networking sites can be exploited to provide some personalized recommendations to the users. These recommendations could be predicting the next point of interest (POI) or stop over in the domain of tourism, judging the future preferences of the users from their past choices or predicting their socio-historical inclination from the data available through location-based social networks (LBSN's). Lot of research has been conducted recently which gives effective recommendation. The goal of this article is to provide a comprehensive survey and comparative analysis of the state-of-art research techniques based extensively on recommendation systems used for applications related to sequence learning.