Recommendation System for Video Streaming Websites Based on User Feedback
Y Divya Bharathi
Y Divya Bharathi*, CSE, GMR Institute of Technology, Rajam, Andhra Pradesh.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1317-1320 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8516088619/2019©BEIESP | DOI: 10.35940/ijeat.F8516.088619
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Now a days world business organizations are mostly focused in e-commerce for enlightening their business as well as supporting their users. In the modern era, vast amount of information is generated from the internet which is open to the users. Recommendation system is midway between internet and user which expects user interests. This paper primarily focusing on developing the recommendation system for video streaming sites. The recommendation engine mainly works on content based, collaborative based filtering algorithms. But both has limitations in their own way. The content-based filtering has a shortcoming that, it restricts recommendations of the items that are of same category. Whereas in the collaborative-based filtering algorithm, it doesn’t recommend items based on the user’s past behavior. So, this system is developed using a hybrid algorithm to overcome the problems of above two algorithms by retrieving feedback from the users and calculating semantic factor from the feedback to improve the efficiency of the recommendation system. So that lets companies can better understand the user, make available personalized stores, and increases the satisfaction of the customer and their loyalty.
Keywords: Content-based filtering, Collaborative based filtering, e-commerce, Recommendation system.