The problem Movie Recommendation System solves

The tagline "Discover your next favorite movie" speaks to the problem that many people face when trying to decide what movie to watch next. With so many options available, it can be overwhelming and time-consuming to sift through them all and find one that aligns with your interests and preferences. A movie recommendation system aims to solve this problem by using algorithms to suggest movies that are likely to appeal to the user based on their past viewing history, ratings, and other factors. By using such a system, users can quickly and easily find new movies to watch without having to spend hours searching for them

Challenges we ran into

Building a movie recommendation system can involve several challenges, including:

Data availability: To build a recommendation system, you need a lot of data, including movie metadata, user ratings, and reviews. It can be challenging to obtain this data, particularly if you don't have access to a large database or if the data is not available in a structured format.

Data quality: Even if you can obtain the necessary data, it may not be of high quality. For instance, user ratings can be biased or inconsistent, which can affect the accuracy of the recommendations.

Cold start problem: A cold start problem occurs when you don't have enough data about a new user or a new movie to make accurate recommendations. For instance, if a user is new to the platform, there may not be enough data about their viewing history to make recommendations.

Overfitting: Overfitting occurs when the recommendation system is too closely tailored to a specific user's preferences, resulting in recommendations that are not diverse enough or don't account for other factors such as genre or popularity.

Scalability: As the number of movies and users grows, it can become more challenging to make accurate recommendations quickly and efficiently.

Overcoming these challenges requires a combination of domain expertise, data science skills, and technical knowledge to build a robust and scalable recommendation system that can provide accurate and diverse recommendations to users.