Recommender Engine - Under the hood

Recommender engine

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
ds = pd.read_csv("test1.csv") #you can plug in your own list of products or movies or books here as csv file#
tf = TfidfVectorizer(analyzer='word', ngram_range=(1, 3), min_df=0, stop_words='english')

#ngram explanation begins#
#ngram (1,3) can be explained as follows#
#ngram(1,3) encompasses uni gram, bi gram and tri gram
#consider the sentence "The ball fell"
#ngram (1,3) would be the, ball, fell, the ball, ball fell, the ball fell
#ngram explanation ends#

tfidf_matrix = tf.fit_transform(ds['Book Title'])
cosine_similarities = linear_kernel(tfidf_matrix, tfidf_matrix)
results = {} # dictionary created to store the result in a dictionary format (ID : (Score,item_id))#
for idx, row in ds.iterrows(): #iterates through all the rows

# the below code 'similar_indice' stores similar ids based on cosine similarity. sorts them in ascending order. [:-5:-1] is then used so that the indices with most similarity are got. 0 means no similarity and 1 means perfect similarity#
    similar_indices = cosine_similarities[idx].argsort()[:-5:-1] 

    #stores 5 most similar books, you can change it as per your needs
    similar_items = [(cosine_similarities[idx][i], ds['ID'][i]) for i in similar_indices]
    results[row['ID']] = similar_items[1:]
    
#below code 'function item(id)' returns a row matching the id along with Book Title. Initially it is a dataframe, then we convert it to a list#
def item(id):
    return ds.loc[ds['ID'] == id]['Book Title'].tolist()[0]
def recommend(id, num):
    if (num == 0):
        print("Unable to recommend any book as you have not chosen the number of book to be recommended")
    elif (num==1):
        print("Recommending " + str(num) + " book similar to " + item(id))
        
    else :
        print("Recommending " + str(num) + " books similar to " + item(id))
        
print("----------------------------------------------------------")
    recs = results[id][:num]
    for rec in recs:
        print("You may also like to read: " + item(rec[1]) + " (score:" + str(rec[0]) + ")")

#the first argument in the below function to be passed is the id of the book, second argument is the number of books you want to be recommended#
recommend(5,2)