Deep Neural Networks for Recommender Systems
Bhakti Ahirwadkar1, Sachin N. Deshmukh2
1Bhakti Ahirwadkar, Department of Computer Science and Engineering, Marathwada Institute of Technology, Aurangabad, Maharashtra, India.
2Sachin N. Deshmukh, Department of Computer Science and IT, Dr Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India.
Manuscript received on September 17, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4838-4842 | Volume-8 Issue-12, October 2019. | Retrieval Number: L37061081219/2019©BEIESP | DOI: 10.35940/ijitee.L3706.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The data available online, helps users to get information about anything of his/her interest. But since the data is huge and complex it is difficult to get useful information from it. Recommender Systems are effective software techniques to overcome this problem. Based on the user’s and item’s information available, these techniques provide recommendations to users in their area of interest. Recommender systems have wide applications like providing suggestive list of items to customers for online shopping, recommending articles or books for online reading, movie or music recommendations, news recommendations etc. In this paper we provide a study of Deep Neural Networks (DNN) approaches that can be used for recommender systems. They have been used widely in last decade in many fields like image processing, video streaming, Natural Language Processing etc. including recommendations to overcome the drawbacks of traditional systems. The paper also provides performance of Denoising AutoEncoders (DAE) which are feed forward neural networks and its comparison with traditional systems. Denoising Autoencoders are a type of autoencoders wherein some part of input is corrupted, i.e., noise is added to the input. While learning to remove noise from input, the DAE also learns to predict unknown values. This property of Denoising Autoencoders can help in recommendation systems to predict unknown values before recommending new items. Experimentation has shown improvement in the performance of recommendation systems with denoising autoencoders. The evaluation is performed on MovieLens-1M dataset with and without additional features of users (age and gender) and items (movie genres) provided in the dataset.
Keywords: Recommender Systems, Collaborative Filtering, Deep Neural Network, Autoencoders, Convolutional Neural Networks, Multi-Layer Perceptron, Denoising Auto Encoders.
Scope of the Article: Neural Information Processing