A Deep Learning Approach towards Cold Start Problem in Movie Recommendation System

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Published: Aug 18, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i8s.7177
Keywords:
Recommendation Systems, Cold start problem, Deep Learning, Artificial Neural Networks, Demographic attributes

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Jinal Panchal
M.Tech, Computer Engineering Department, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India
Sandeep Vanjale
HoD and Professor, Computer Engineering Department, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India

Abstract

Recommendation systems play an important role for e-commerce websites to make profits. It has a variety of applications in different domains. There are three types of categories in which recommendation systems are classified i.e. content based, collaborative and hybrid systems. These systems suffer when a redundant amount of information is not available to provide recommendations. This problem is known as the cold start problem. In this digital era, it is possible to collect meta information about a user and provide rich recommendations. Various approaches such as social media analysis, graph networks have been proposed to solve this problem. But they lack personalization and generate irrelevant recommendations affecting the system performance. The objective of this work is to resolve new user cold start problem in movie recommendation systems using a deep learning approach that utilizes demographic attributes to cluster similar users. This embedding is given to the deep neural network to generate the recommendations. From the analysis done, we verify the effectiveness of our approach..

Article Details

How to Cite
Panchal, J. ., & Vanjale, S. . (2023). A Deep Learning Approach towards Cold Start Problem in Movie Recommendation System. International Journal on Recent and Innovation Trends in Computing and Communication, 11(8s), 81–85. https://doi.org/10.17762/ijritcc.v11i8s.7177
Issue
Vol. 11 No. 8s (2023)
Section
Articles

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