Rekomendasi profil pengguna
Membangun Recommendation Engine di Python
Rob O'Callaghan
Director of Data
Rekomendasi item ke item
Profil pengguna
tfidf_summary_df:
| Book | Adventure | Fantasy | Tragedy | Social commentary |
|---|---|---|---|---|
| The Hobbit | 1 | 1 | 0 | 0 |
| Macbeth | 0 | 0 | 1 | 0 |
| ... | ... | ... | ... | ... |
Profil Pengguna:
| Profil Pengguna | Adventure | Fantasy | Tragedy | Social commentary |
|---|---|---|---|---|
| User_001 | ??? | ??? | ??? | ??? |
Ekstrak data pengguna
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 ...
Bangun profil pengguna
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, ...]
Menemukan rekomendasi untuk pengguna
# 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"])
Mengambil rekomendasi teratas
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
... ...
Ayo berlatih!
Membangun Recommendation Engine di Python
