外文文献：Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications
Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications
Bharadwaja Krishnadev Mylavarapu*
John Deere Co., Moline, IL, USA
How to cite this paper: Mylavarapu, B.K. (2018) Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications. Journal
of Computer and Communications, 6,
1-14.
https://doi.org/10.4236/jcc.2018.612001
Received: November 12, 2018
Accepted: December 9, 2018
Published: December 12, 2018
Copyright copy; 2018 by author and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/
Open Access
Abstract
To make recommendation on items from the user for historical user rating several intelligent systems are using. The most common method is Recom- mendation systems. The main areas which play major roles are social net- working, 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 com- plete cold start (CCS) problem and incomplete cold start (ICS) problem. The authors proposed three novel methods such as collaborative filtering, and ar- tificial 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 tempor- al dynamics is used to obtain the contented characteristics into predictions to overcome those problems. For the process of classification we have used li- near support vector machine classifiers to receive the better performance when compared with the earlier methods.
*Business Analytics Lead, Analytics, PLM COE, PDM.
B. K. Mylavarapu
Keywords
Artificial Neural Network, Support Vector Machine, Recommendation Systems, Cold Start Problems
Introduction
Artificial Neural Network
We can regard this network as a computational model which is based on the configuration as well as purpose of biological neural networks. The mechanism of artificial neural network is human brain processes information which contains a huge amount of associated processing units to facilitate effort simultaneously towards progression information; since, they also produce significant conse- quences and it consists of the following 3 layers which are shown in Figure 1 as:
Input layer—The function of the input layer is to accept and input the values
of the descriptive characteristic used for apiece examination. The general thing in this layer is number of input nodes which is equivalent to the number of de- scriptive variables. To converse one or more “concealed layers”, the prototype to the network was obtainable by the “Input layer”.
Hidden layer—Within the network, the specified conversion to the input
values has been applied by the Hidden layers. By using the hidden layer, and ar- riving curves with the intention of departing as additional concealed nodes or else, the input nodes associated to each node. Through the system of weighted “connections”, actual processing is done in hidden layer and it might have the possibility of one or more hidden layers.
Output layer—From concealed layers or else input layer, the connections are
received by the output layer. It precedes an output value with the intention of communicate towards the prophecy of the retort patchy.
Linear Support Vector Machine
Based on the idea of decision planes SVM can define the decision boundaries. The separation among set of objects with irrelevant class memberships is re- ferred as decision plane. The example is given in Figure 2.
Figure 1. Artificial neural network.
B. K. Mylavarapu
John Deere Co., Moline, IL, USA
of Computer and Communications, 6,
lt;a id='_bookmark
1-14.
https://doi.org/10.4236/jcc.2018.612001
Received: November 12, 2018
Accepted: December 9, 2018
Published: December 12, 2018
Copyright copy; 2018 by author and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/
Open Access
Abstract
To make recommendation on items from the user for historical user rating several intelligent systems are using. The most common method is Recom- mendation systems. The main areas which play major roles are social net- working, 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 com- plete cold start (CCS) problem and incomplete cold start (ICS) problem. The authors proposed three novel methods such as collaborative filtering, and ar- tificial 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 tempor- al dynamics is used to obtain the contented characteristics into predictions to overcome those problems. For the process of classification we have used li- near support vector machine classifiers to receive the better performance when compared with the earlier methods.
*Business Analytics Lead, Analytics, PLM COE, PDM.
B. K. Mylavarapu
Keywords
Artificial Neural Network, Support Vector Machine, Recommendation Systems, Cold Start Problems
Introduction
Artificial Neural Network
We can regard this network as a computational model which is based on the configuration as well as purpose of biological neural networks. The mechanism of artificial neural network is human brain processes information which contains a huge amount of associated processing units to facilitate effort simultaneously towards progression information; since, they also produce significant conse- quences and it consists of the following 3 layers which are shown in Figure 1 as:
Input layer—The function of the input layer is to accept and input the values
of the descriptive characteristic used for apiece examination. The general thing in this layer is number of input nodes which is equivalent to the number of de- scriptive variables. To converse one or more “concealed layers”, the prototype to the network was obtainable by the “Input layer”.
Hidden layer—Within the network, the specified conversion to the input
values has been applied by the Hidden layers. By using the hidden layer, and ar- riving curves with the intention of departing as additional concealed nodes or else, the input nodes associated to each node. Through the system of weighted “connections”, actual processing is done in hidden layer and it might have the possibility of one or more hidden layers.
Output layer—From concealed layers or else input layer, the connections are
received by the output layer. It precedes an output value with the intention of communicate towards the prophecy of the retort patchy.
Linear Support Vector Machine
Based on the idea of decision planes SVM can define the decision boundaries. The separation among set of objects with irrelevant class memberships is re- ferred as decision plane. The example is given in Figure 2.
Figure 1. Artificial neural network.
B. K. Mylavarapu
Figure 2. Linear SVM.
A sample of linear classifier is shown above. Moreover, all classification task are not merely, as well as there is a necessity for more difficult structures in se- quence to produce an optimal separation. A hyperlane classifier defines that the classification errands based on depiction sort
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