With the ability of machine learning, we explore the dataset to create a recommendation system with the ability to help someone to find what to watch next.

Every streaming content has its own viewers and each content has it’s rating. Viewers leave some good ratings for the content if they like it. But where does it apply? Viewers can spend hours scrolling through hundreds, sometimes thousands of anime's, never finding any content they like. Businesses need to provide suggestions based on their likes and needs in order to create a better streaming environment that boosts revenue and increases the time spent on a website.

Implement Collaborative Filtering and content based filtering for recommendation systems.

Using an anime dataset, to create a recommendation system based on two famous algorithms of content based and collaborative filtering recommendation systems. These algorithms are implemented from scratch with other variations to overcome limitations of each other.

• For this project I have downloaded anime and user ratings from: https://github.com/Hernan4444/MyAnimeList-Database.
• This dataset contains information about 17,562 anime and the preference from 57,633,278 different users.
• Ratings given by users to the anime that they have watched completely.
• Information about the anime like genre, stats, studio, etc.
• Synopsis of the anime as per myanimelist.com
• Ratings are in range from 1 - 10 with global avg rating of 7.80

Python: For writing basic algorithms using data structures of python. Pandas: Pandas dataframe for querying over the data tables. Skit-learn:To implement TF_IDF, Similarities and other metric operations Matplotlib: For visualizing the data.

This algorithm recommends products which are similar to the ones that a user has liked in the past. The content of each item is represented as a set of descriptors or terms, typically the words that occur in a document.A content based recommender works with data that the user provides, either explicitly (rating) or implicitly (clicking on a link). Based on that data, a user profile is generated, which is then used to make suggestions to the user. As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate

They are broadly based on 2 category:

1. User Based: Takes user_id to get recommendation
2. Item Based: Give recommendation based on the serch We use cosine similarity to find the relation Cosine similarity metric measures the cosine of the angle between two n-dimensional vectors projected in a multi-dimensional space. The Cosine similarity of two documents will range from 0 to 1.

Collaborative filtering is a technique that can filter out items that a user might like on the basis of reactions by similar users. It works by searching a large group of people and finding a smaller set of users with tastes similar to a particular user.

1. User-User collaborative filtering- This algorithm first finds the similarity score between users. Based on this similarity score, it then picks out the most similar users and recommends products which these similar users have liked or bought previously.
2. Item-Item collaborative filtering: In this algorithm, we compute the similarity between each pair of items. So in our case we will find the similarity between each movie pair and based on that, we will recommend similar movies which are liked by the users in the past.

A major drawback of content based filtering algorithm is that it is limited to recommending items that are of the same type. It will never recommend products which the user has not bought or liked in the past. So if a user has watched or liked only action movies in the past, the system will recommend only action movies. It’s a very narrow way of building an engine. To improve on this type of system, we need an algorithm that can recommend items not just based on the content, but the behavior of users as well. In practice, many commercial recommender systems are based on large datasets. As a result, the user-item matrix used for collaborative filtering could be extremely large and sparse, which brings about challenges in the performance of the recommendation.One typical problem caused by the data sparsity is the cold start problem. As collaborative filtering methods recommend items based on users' past preferences, new users will need to rate a sufficient number of items to enable the system to capture their preferences accurately and thus provides reliable recommendations. Similarly, new items also have the same problem. When new items are added to the system, they need to be rated by a substantial number of users before they could be recommended to users who have similar tastes to the ones who rated them. The new item problem does not affect content-based recommendations, because the recommendation of an item is based on its discrete set of descriptive qualities rather than its ratings.

Creating a hybrid recommendation system that that combines content-based filtering and collaborative filtering could potentially take advantage from both the representation of the content as well as the similarities among users and negates the limitations. Testing the model with the appropriate algorithm.

https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/ https://github.com/Hernan4444/MyAnimeList-Database https://www.analyticssteps.com/blogs/what-are-recommendation-systems-machine-learning https://builtin.com/data-science/recommender-systems