Product recommendation: A deep learning factorization method using separate learners
Bishwajit Purkaystha, Tapos Datta, Md. Saiful Islam, Marium-E-Jannat · 2017
Exponential growth in information has made it totally unimaginable to manually find a relevant product in a quick time, entailing the need for a mechanical recommendation system which would remember the users and recommend most suitable items. Most of the approaches for such machinery have been to first find similarity in users or in items, and then exploit these similarities to recommend the products. These methods produce better results when demographic information about users and items are given to them. In this paper, we propose a deep neural network model which does not require any information be given to it other than the rating triples. We created spurious user profiles and item characteristics by using separate learner weights at the bottommost layer. The weights in the upper layers took these information, created by the weights at bottommost layer, to produce a real valued rating. Our model produced an RMSE 4.1824 on Jester 4-million datasti, and this shows our deep network is comparable to the state of the art models.