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tobiasaurer
commited on
Commit
β’
8b2ad1d
1
Parent(s):
c86a927
adds new recommender and improves old ones
Browse files
pages/{1 - Popularity based recommender.py β 1 - Popularity-Based Recommender.py}
RENAMED
@@ -10,7 +10,7 @@ st.title("Popularity-Based Recommender")
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st.write("""
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### Instructions
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Move the slider to the desired number of recommendations you wish to receive.
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Afterwards, simply click the "
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If you want, you can narrow it down by picking one or several genre(s).
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""")
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@@ -43,5 +43,5 @@ genres = st.multiselect('Optional: Select one or more genres', genre_list, defau
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genres_regex = transform_genre_to_regex(genres)
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# EXECUTION:
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if st.button("
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st.write(get_popular_recommendations(number_of_recommendations, genres_regex))
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st.write("""
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### Instructions
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Move the slider to the desired number of recommendations you wish to receive.
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Afterwards, simply click the "Get Recommendations" button to receive recommendations of the most popular movies in our database.
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If you want, you can narrow it down by picking one or several genre(s).
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""")
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genres_regex = transform_genre_to_regex(genres)
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# EXECUTION:
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if st.button("Get Recommendations"):
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st.write(get_popular_recommendations(number_of_recommendations, genres_regex))
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pages/{2 - User based recommender.py β 2 - User-Based Recommender.py}
RENAMED
@@ -23,7 +23,8 @@ st.title("User-Based Recommender")
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st.write("""
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### Instructions
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Type in the user-ID you want to receive recommendations for.
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Move the slider to the desired number of recommendations you wish to receive.
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""")
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st.write("""
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Optional: You can narrow down the recommendations by picking one or several genre(s).
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@@ -51,9 +52,9 @@ def get_user_recommendations(user_id, n, genres):
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recommendations = (
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weighted_averages
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.sort_values("predicted_rating", ascending=False)
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.head(n)
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.merge(movies, how= 'left', left_index = True, right_on = 'title')
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[lambda df: df["genres"].str.contains(genres, regex=True)]
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)
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return recommendations[['title', 'genres']]
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@@ -74,5 +75,5 @@ genres = st.multiselect('Optional: Select one or more genres', genre_list, defau
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genres_regex = transform_genre_to_regex(genres)
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# EXECUTION:
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if st.button("
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st.write(get_user_recommendations(user_id_input, number_of_recommendations, genres_regex))
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st.write("""
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### Instructions
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Type in the user-ID you want to receive recommendations for.
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Move the slider to the desired number of recommendations you wish to receive.
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Afterwards, simply click the "Get Recommendations" button to receive recommendations that are most suitable for the given user.
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""")
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st.write("""
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Optional: You can narrow down the recommendations by picking one or several genre(s).
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recommendations = (
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weighted_averages
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.sort_values("predicted_rating", ascending=False)
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.merge(movies, how= 'left', left_index = True, right_on = 'title')
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[lambda df: df["genres"].str.contains(genres, regex=True)]
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.head(n)
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)
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return recommendations[['title', 'genres']]
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genres_regex = transform_genre_to_regex(genres)
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# EXECUTION:
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if st.button("Get Recommendations"):
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st.write(get_user_recommendations(user_id_input, number_of_recommendations, genres_regex))
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pages/3 - Similarity-Based Recommender.py
ADDED
@@ -0,0 +1,84 @@
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import streamlit as st
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import pandas as pd
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from sklearn.metrics.pairwise import cosine_similarity
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import re
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# DATA:
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movies = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/recommender-systems/main/movie_data/movies.csv')
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ratings = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/recommender-systems/main/movie_data/ratings.csv')
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# create "database" to use for recommendations
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movie_user_matrix = (
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ratings
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.merge(movies, on='movieId')[['title', 'rating', 'userId']]
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.pivot_table(index='title', columns='userId', values='rating')
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.fillna(0)
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)
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similarities_movies = pd.DataFrame(cosine_similarity(movie_user_matrix),
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index=movie_user_matrix.index,
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columns=movie_user_matrix.index)
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# INSTRUCTIONS:
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st.title("User-Based Recommender")
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st.write("""
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### Instructions
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Type in the title of a movie for which you would like to receive similar recommendations.
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Move the slider to the desired number of recommendations you wish to receive.
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Afterwards, simply click the "Get Recommendations" button to receive recommendations that are most similar to the given movie.
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""")
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st.write("""
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Optional: You can narrow down the recommendations by picking one or several genre(s).
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Note: The more genres you choose, the fewer movies will be recommended.
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""")
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# FUNCTIONS:
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def get_similar_recommendations(movie_title, n, genres):
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# select similarity for chosen movie
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similarities = pd.DataFrame(similarities_movies.loc[similarities_movies.index != movie_title, movie_title])
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# exclude genres if necessary and return the n movies with the highest similarity
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recommendations = (
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similarities
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.merge(movies, how= 'left', left_index = True, right_on = 'title')
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[lambda df: df["genres"].str.contains(genres, regex=True)]
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.head(n)
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[['title', 'genres']]
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)
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return recommendations
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def transform_genre_to_regex(genres):
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regex = ""
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for genre in genres:
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regex += f"(?=.*{genre})"
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return regex
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def find_movie_title(user_input):
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title_list = movies.title.unique()
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r = re.compile(f".*{user_input}.*")
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result = []
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for title in title_list:
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match = r.findall(title)
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if match:
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result.append(match)
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return result[0][0]
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# USER INPUT:
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movie_title_raw = st.text_input('Movie Title')
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movie_title = find_movie_title(movie_title_raw)
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number_of_recommendations = st.slider("Number of recommendations", 1, 10, 5)
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genre_list = set([inner for outer in movies.genres.str.split('|') for inner in outer])
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genres = st.multiselect('Optional: Select one or more genres', genre_list, default=None, key=None, help=None, on_change=None, args=None, kwargs=None, disabled=False)
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genres_regex = transform_genre_to_regex(genres)
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# EXECUTION:
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if st.button("Get Recommendations"):
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st.write(get_similar_recommendations(movie_title, number_of_recommendations, genres_regex))
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requirements.txt
CHANGED
@@ -1,3 +1,4 @@
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streamlit
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pandas
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scikit-learn
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streamlit
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pandas
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scikit-learn
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re
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