Recommendation systems are everywhere right now like Amazon, Netflix, and Airbnb. So, probably that would make you wonder that how these engines work, so in this article I will try to explain the Popularity based recommendation system. Types of recommendation systems are as follows: Popularity based recommendation system Collaborative recommendation system Content-based recommendation system Demographic-based recommendation system Utility-based recommendation system Knowledge-based recommendation system Hybrid-recommendation system Popularity based recommendation system As the name suggests Popularity based recommendation system works with the trend. It basically uses the items which are in trend right now. For example, if any product which is usually bought by every new user then there are chances that it may suggest that item to the user who just signed up. There are some problems as well with the popularity based recommender system and it also solves some of the problems with it as well. The problems with popularity based recommendation system is that the personalization is not available with this method i.e. even though you know the behaviour of the user you cannot recommend items accordingly. So, I hope you now have enough idea about the popularity based recommendation system. So, let’s get our hand dirty with the code. The link to my notebook and data is . here Let’s begin the coding part import pandas import numpy as np import Recommender First of all we will import the numpy and the pandas which we are going to use a lot and the that we created which includes the methods like which basically creates the recommendations and the which recommends the items to the user. class create recommend triplets_file = 'https://static.turi.com/datasets/millionsong/10000.txt' songs_metadata_file = 'song_data.csv' In this section we import our dataset as a and The contains . The contains . triplets_file songs_metadata_file. triplet_file user_id, song_id and listen_count songs_metadata_file song_id, title, release_by and artist_name song_df_1 = pandas.read_table(triplets_file,header= )song_df_1.columns = ['user_id', 'song_id', 'listen_count'] None After that we have to merge the two datasets that we imported. song_df_2 = pandas.read_csv(songs_metadata_file)song_df = pandas.merge(song_df_1, song_df_2.drop_duplicates(['song_id']), on="song_id", how="left") In this section we merged the two by droping the dupliactes song_id. song_df = song_df.head(10000) Because it is a large dataset I have only considered first 10k rows. After changing the length of dataset I have done some additional modification. song_df['song'] = song_df['title'].map(str) + " - " + song_df['artist_name'] In which I included the one column named and which concatenates the title and the artist of the song. song song_df_grouped = song_df.groupby(['song']).agg({'listen_count': 'count'}).reset_index()song_df_grouped.sort_values('listen_count',ascending = 0) In the step shown above the dataset grouped using the pandas function by field and aggregated with the field and after that the values are sorted in non increasing order according to the groupby song listen_count listen_count. users = song_df['user_id'].unique()len(users) items = song_df['song'].unique()len(items) This step finds out the unique users and items in the dataset. from sklearn.cross_validation import train_test_splittrain_data, test_data = train_test_split(song_df, test_size = 0.20, random_state=0) This section of code splits the dataset into training and the test dataset using 80–20 ratio. pr = Recommender.Popularity_Recommender()pr.create(train_data, 'user_id', 'song') Here, the instance is created of the class The function takes the three parameters the training data, user id for which the recommendation is created and the column of the item for which you want to make recommendation in our case it is Popularity_Recommender(). create song. pr.recommend(users[5]) As I said before that the method recommend gives the recommendation to the user which is passed as a parameter. So, it returns the list of the popular songs for the user but since it is popularity based recommendation system the recommendation for the users will not be affected. Result of the recommendation system for 6th user pr.recommend(users[100]) So, as you can see here that although if we change the user the result that we get from the system is the same since it is a popularity based recommendation system. Result of the recommendation system for 99th user Don’t forget to clap and put down your thoughts about the article. Thank you :) Source: Divya Sardana | Building Recommender Systems Using Python https://github.com/llSourcell/recommender_live
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