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Wei, J, He., J.H., Chen, K., Zhou, Y. and Tang, Z.Y. (2016) Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem. 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, Auckland, 8-12 August 2016, 874-877.
https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.149

has been cited by the following article:

• TITLE: Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications

  AUTHORS: Bharadwaja Krishnadev Mylavarapu

  KEYWORDS: Artificial Neural Network, Support Vector Machine, Recommendation Systems, Cold Start Problems

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.12, December 12, 2018

  ABSTRACT: To make recommendation on items from the user for historical user rating several intelligent systems are using. The most common method is Recommendation systems. The main areas which play major roles are social networking, digital marketing, online shopping and E-commerce. Recommender system consists of several techniques for recommendations. Here we used the well known approach named as Collaborative filtering (CF). There are two types of problems mainly available with collaborative filtering. They are complete cold start (CCS) problem and incomplete cold start (ICS) problem. The authors proposed three novel methods such as collaborative filtering, and artificial neural networks and at last support vector machine to resolve CCS as well ICS problems. Based on the specific deep neural network SADE we can be able to remove the characteristics of products. By using sequential active of users and product characteristics we have the capability to adapt the cold start product ratings with the applications of the state of the art CF model, time SVD++. The proposed system consists of Netflix rating dataset which is used to perform the baseline techniques for rating prediction of cold start items. The calculation of two proposed recommendation techniques is compared on ICS items, and it is proved that it will be adaptable method. The proposed method can be able to transfer the products since cold start transfers to non-cold start status. Artificial Neural Network (ANN) is employed here to extract the item content features. One of the user preferences such as temporal dynamics is used to obtain the contented characteristics into predictions to overcome those problems. For the process of classification we have used linear support vector machine classifiers to receive the better performance when compared with the earlier methods.