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我是编码的绝对初学者。有这本书推荐系统，但我想在这里做一个小改动。目前，程序在收到用户的书名后会推荐相似的书，但我希望它以用户的书名作为输入，并在将用户的输入与 csv 文件中的书名进行比较后找到相似的书名。任何关于代码如何工作的分步指南也将不胜感激:)。这是代码

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
ds = pd.read_csv("test1.csv") 
tf = TfidfVectorizer(analyzer='word', ngram_range=(1, 3), min_df=0, stop_words='english')


tfidf_matrix = tf.fit_transform(ds['Book Title'])
cosine_similarities = cosine_similarity(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)
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