User profile recommendations
Building Recommendation Engines in Python
Rob O'Callaghan
Director of Data
Item to item recommendations
User profiles
tfidf_summary_df:
| Book | Adventure | Fantasy | Tragedy | Social commentary |
|---|---|---|---|---|
| The Hobbit | 1 | 1 | 0 | 0 |
| Macbeth | 0 | 0 | 1 | 0 |
| ... | ... | ... | ... | ... |
User Profile:
| User Profile | Adventure | Fantasy | Tragedy | Social commentary |
|---|---|---|---|---|
| User_001 | ??? | ??? | ??? | ??? |
Extract the user data
list_of_books_read = ['The Hobbit', 'Foundation', 'Nudge']user_books = tfidf_summary_df.reindex(list_of_books_read)print(user_books)
age ancient angry brave battle fellow ...
The Hobbit 0.21 0.53 0.41 0.64 0.01 0.02 ...
Foundation 0.31 0.90 0.42 0.33 0.64 0.04 ...
Nudge 0.61 0.01 0.45 0.31 0.12 0.74 ...
Build the user profile
user_prof = user_movies.mean()print(user_prof)
age 0.376667
ancient 0.480000
angry 0.426667
brave 0.256667
...
print(user_prof.values.reshape(1,-1))
[0.376667, .480000, 0.426667, 0.256667, ...]
Finding recommendations for a user
# Create a subset of only the non read books non_user_movies = tfidf_summary_df.drop(list_of_movies_seen, axis=0)# Calculate the cosine similarity between all rows user_prof_similarities = cosine_similarity(user_prof.values.reshape(1, -1), non_user_movies)# Wrap in a DataFrame for ease of use user_prof_similarities_df = pd.DataFrame(user_prof_similarities.T, index=tfidf_summary_df.index, columns=["similarity_score"])
Getting the top recommendations
sorted_similarity_df = user_prof_similarities.sort_values(by="similarity_score", ascending=False)print(sorted_similarity_df)
similarity_score
Title
The Two Towers 0.422488
Dune 0.363540
The Magicians Nephew 0.316075
... ...
Let's practice!
Building Recommendation Engines in Python
