Deep convolutional neural networks for product recommendation

Deep convolutional neural networks for product recommendation

N. Lakshmipathi Anantha Bhanu Prakash Battula 

Acharya Nagarjuna University, Department of IT, VFSTR Deemed to be University, Vadlamudi, India

Department of CSE, Thirumala Engineering College, Jonnalagadda (V), India anlakshmipathi@gmail.com

Corresponding Author Email: 
anlakshmipathi@gmail.com
Page: 
161-172
|
DOI: 
https://doi.org/10.3166/ISI.23.6.161-172
Received: 
|
Accepted: 
|
Published: 
31 December 2018
| Citation

OPEN ACCESS

Abstract: 

Recommender Systems are big breakthrough for the web enabled systems as Recommender Systems have the capability to analyze the behavior patterns of the user. And these systems are accomplishing the task of recommending the products the users are interested in. Existed models grabbing the insights of the users and items patterns will give satisfactory results to the users. This paper uses pretrained models to extract the knowledge from the data using the concept of transfer learning. Our models use the knowledge of pre-trained models to extract patterns between users and items. To facilitate this objective, in this paper we presented our approach to generate recommendations in two phases. In the Classification phase, classification of product images and its experimental analysis following, the Ranking phase to rank the product images to the user and its experimental analysis are discussed. The result analysis discussed in this paper achieved promising results.

Keywords: 

recommender system, convolutional neural network, content-based filtering, ranking

1. Introduction
2. Related work
3. Classification phase
4. Ranking phase
5. Conclusion
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