Movie-recommend-system
Recommendation systems are Artificial Intelligence based algorithms that skim through all possible options and create a customized list of items that are interesting and relevant to an individual
Created on 9th July 2024
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Movie-recommend-system
Recommendation systems are Artificial Intelligence based algorithms that skim through all possible options and create a customized list of items that are interesting and relevant to an individual
The problem Movie-recommend-system solves
Recommendation systems are becoming increasingly important in today’s extremely busy world. People are always short on time with the myriad tasks they need to accomplish in the limited 24 hours. Therefore, the recommendation systems are important as they help them make the right choices, without having to expend their cognitive resources. The purpose of a recommendation system basically is to search for content that would be interesting to an individual. Moreover, it involves a number of factors to create personalised lists of useful and interesting content specific to each user/individual. Recommendation systems are Artificial Intelligence based algorithms that skim through all possible options and create a customized list of items that are interesting and relevant to an individual. These results are based on their profile, search/browsing history, what other people with similar traits/demographics are watching, and how likely are you to watch those movies. This is achieved through predictive modeling and heuristics with the data available.
Challenges we ran into
Streamlit was new to us as we have never used before but during this hackathon we found a brief way to implement our streamlit app. It was thus been easy to use our Machine Learning models in our apps and was able to grasp this new tech stack. Also, some of the jupyter commands like creating a pickle file and to use it in our streamlit application.
Tracks Applied (2)
Polygon Track
Polygon
Ethereum Track
ETHIndia