Book Recommendation Engine using KNN

Book Recommendation Engine using KNN

https://www.freecodecamp.org/learn/machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn

In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors.

You will use the Book-Crossings dataset. This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.

You can access the full project instructions and starter code on Google Colaboratory.

Pandas

https://www.cnblogs.com/beyondChan/p/10861045.html

https://www.cnblogs.com/feily/p/14397470.html

NearestNeighbors

Unsupervised learner for implementing neighbor searches.

Read more in the User Guide.

Examples
>>>

>>> import numpy as np
>>> from sklearn.neighbors import NearestNeighbors
>>> samples = [[0, 0, 2], [1, 0, 0], [0, 0, 1]]

>>>

>>> neigh = NearestNeighbors(n_neighbors=2, radius=0.4)
>>> neigh.fit(samples)
NearestNeighbors(...)

>>>

>>> neigh.kneighbors([[0, 0, 1.3]], 2, return_distance=False)
array([[2, 0]]...)

>>>

>>> nbrs = neigh.radius_neighbors(
...    [[0, 0, 1.3]], 0.4, return_distance=False
... )
>>> np.asarray(nbrs[0][0])
array(2)

参考

https://datascienceplus.com/building-a-book-recommender-system-the-basics-knn-and-matrix-factorization/

使用 pivot 建立二维虚拟视图。

us_canada_user_rating = us_canada_user_rating.drop_duplicates(['userID', 'bookTitle'])
us_canada_user_rating_pivot = us_canada_user_rating.pivot(index = 'bookTitle', columns = 'userID', values = 'bookRating').fillna(0)
us_canada_user_rating_matrix = csr_matrix(us_canada_user_rating_pivot.values)

from sklearn.neighbors import NearestNeighbors

model_knn = NearestNeighbors(metric = 'cosine', algorithm = 'brute')
model_knn.fit(us_canada_user_rating_matrix)

query_index = np.random.choice(us_canada_user_rating_pivot.shape[0])
distances, indices = model_knn.kneighbors(us_canada_user_rating_pivot.iloc[query_index, :].reshape(1, -1), n_neighbors = 6)

for i in range(0, len(distances.flatten())):
    if i == 0:
        print('Recommendations for {0}:
'.format(us_canada_user_rating_pivot.index[query_index]))
    else:
        print('{0}: {1}, with distance of {2}:'.format(i, us_canada_user_rating_pivot.index[indices.flatten()[i]], distances.flatten()[i]))

https://zhuanlan.zhihu.com/p/29903232

https://github.com/BUVANEASH/Book-Recommendation---Collaborative-Filtering

https://github.com/jalajthanaki/Book_recommendation_system/blob/master/KNN_based_recommendation_system.ipynb