INTELLIGENT RECOMMENDATION SYSTEM A COMPREHENSIVE STUDY ON DEEP LEARNING BASED CF MODEL
The models based on collaborative filtering (CF) are capable of understanding the relationship or connection between the users and objects being considered. Established CF based systems, however, can only understand a particular form of relationship, such as a restricted Boltzmann system that seizes the user user connection or item item relationship distinctly. At the other hand, factorization of matrixes specifically describes the relation between them.To address these shortcomings in CF based approaches, we suggest a novel approach of deep learning that mimics an efficient smart suggestion through previous knowledge of users and objects. In the initial level, the related low dimensional object vectors and objects are studied separately, which embeds the semantic knowledge representing the association between the object and the item item. Feed forward neural networks are used during the prediction stage to model the relationship between user and object, where the corresponding pretrained representational vectors are used as inputs of the neural networks. Several experiments based on two benchmark datasets (MovieLens 1 M and MovieLens 10 M) are conducted to verify the effectiveness of the proposed method, and the result shows that our model outperforms previous methods using feed forward neural networks by a significant margin and performs very similarly on both datasets to state of the art methods.