Movie Recommender

Inspiration

A Recommendation System is a filtration program whose prime goal is to predict the “rating” or “preference” of a user towards a domain-specific item or item. In our case, this domain-specific item is a movie, therefore the main focus of our recommendation system is to filter and predict only those movies which a user would prefer given some data about the user him or herself.

What it does

It filters the movies according to the people wants.....

How we built it

1.First, we need to import libraries which we’ll be using in our movie recommendation system. Also, we’ll import the dataset by adding the path of the CSV files. 2.Now that we have added the data, let’s have a look at the files using the dataframe.head() command to print the first 5 rows of the dataset. 3.Movie dataset has

movieId – once the recommendation is done, we get a list of all similar movieId and get the title for each movie from this dataset. genres – which is not required for this filtering approach

Challenges we ran into

1.Cold start 2.Sparsity 3.Synonymy 4.Privacy 5.Scalability

1. Latency

Accomplishments that we're proud of

The recommending system that provides new ways of finding or extracting personalized information on the internet. It enables users to have access to the products and services easily within limited periods of time.

What we learned

We learned how to create a Movies Recommender

What's next for Movie Recommender

1.Drive Traffic. ... 2.Deliver Relevant Content. ... 3.Engage Shoppers. ... 4.Convert Shoppers to Customers. ... 5.Increase Average Order Value. ... 6.Increase Number of Items per Order. ... 7.Control Merchandising and Inventory Rules. ... 8.Reduce Workload and Overhead.

Built With

• python