What are Recommendation Systems
Recommender systems are systems that are designed to recommend things to the user based on many different factors. These systems predict the most likely product the users are most likely to purchase and are of interest to. Companies like Netflix, Amazon, etc. use recommender systems to help their users identify the correct products or movies for them.
The recommender system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between the user and the item and imputes the similarities between users and items for recommendation.
Collaborative filtering systems use the actions of users to recommend other movies. In general, they can either be user-based or item-based. Item-based approach is usually preferred over a user-based approach. The user-based approach is often harder to scale because of the dynamic nature of users. In contrast, items usually don’t change much, and an item-based approach often can be computed offline and served without constantly re-training.
Getting the Data For Our Project
As We are Applying the Recommendation system to Anime we have to first get an Anime dataset where Kaggle Comes into play. After going through many datasets I came across this dataset.
Analyzing and Preprocessing The Dataset
I will now merge the rating data and anime_contact_data(data extracted from anime_data) in terms of anime_id.
if you see The Data Carefully many users have only voted on a few anime series and some anime series have very few votes and I think this can mislead our model. we will take only that data in which a particular anime has more than 200 Votes and if a user has given in total of more than 500 Votes to the anime.
We will now create a pivot table based on the Name and User_id columns and save it into a variable name piviot_table
Now we can move to make a model for our recommendation system.
Model Creation
Implementing KNN
We convert our table to a 2D matrix, and fill the missing values with zeros (since we will calculate distances between rating vectors). We then transform the values(ratings) of the matrix data frame a scipy sparse matrix for more efficient calculations.
Finding the Nearest Neighbors
We use unsupervised algorithms with sklearn. neighbors. The algorithm we use to compute the nearest neighbors is, and we specify metric=cosine so that the algorithm will calculate the cosine similarity between rating vectors. Finally, we fit the model.
Test our model and make some recommendations:
The KNN algorithm measures distance to determine the closeness of instances. It then classifies an instance by finding its nearest neighbors and picks the most popular class among the neighbors.
I will create a Predict() function so that every time I call this function it will recommend me the 5 closest recommendations.
RESULT
Outgo and Resources for further Experiment.
If you Liked This Article and you have some doubt If you want a Brief Explanation then please Consider Checking My Kaggle Page where I have briefly discussed this topic if you want the Source Code for this Project Please Visit my GitHub Repository. I also hosted This project as a Web App the link you will get is in my GitHub repository.
Video Demo Of the Web App
