Hands-On Recommendation Systems with Python by Rounak Banik

Author:Rounak Banik
Language: eng
Format: epub
Tags: COM004000 - COMPUTERS / Intelligence (AI) and Semantics, COM042000 - COMPUTERS / Natural Language Processing, COM018000 - COMPUTERS / Data Processing
Publisher: Packt Publishing
Published: 2018-07-31T08:18:12+00:00

#Compute the cosine similarity score (equivalent to dot product for tf-idf vectors)

cosine_sim2 = cosine_similarity(count_matrix, count_matrix)

Since we dropped a few movies with bad indices, we need to construct our reverse mapping again. Let's do that as the next step:

# Reset index of your df and construct reverse mapping again

df = df.reset_index()

indices2 = pd.Series(df.index, index=df['title'])

With the new reverse mapping constructed and the similarity scores computed, we can reuse the content_recommender function defined in the previous section by passing in cosine_sim2 as an argument. Let's now try out our new model by asking recommendations for the same movie, The Lion King:

content_recommender('The Lion King', cosine_sim2, df, indices2)

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