Aanbevelingen op basis van gebruikersprofielen
Aanbevelingssystemen bouwen in Python
Rob O'Callaghan
Director of Data
Item-naar-item-aanbevelingen
Gebruikersprofielen
tfidf_summary_df:
| Boek | Avontuur | Fantasy | Tragedie | Maatschappijkritiek |
|---|---|---|---|---|
| The Hobbit | 1 | 1 | 0 | 0 |
| Macbeth | 0 | 0 | 1 | 0 |
| ... | ... | ... | ... | ... |
Gebruikersprofiel:
| Gebruikersprofiel | Avontuur | Fantasy | Tragedie | Maatschappijkritiek |
|---|---|---|---|---|
| User_001 | ??? | ??? | ??? | ??? |
Gebruikersdata ophalen
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 ...
Stel het gebruikersprofiel op
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, ...]
Aanbevelingen voor een gebruiker vinden
# 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"])
De beste aanbevelingen ophalen
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
... ...
Laten we oefenen!
Aanbevelingssystemen bouwen in Python
