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tobiasaurer
commited on
Commit
•
c86a927
1
Parent(s):
b4c7df7
adds new recommender
Browse files
pages/1 - Popularity based recommender.py
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import streamlit as st
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import pandas as pd
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#
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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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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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@@ -42,7 +42,6 @@ genre_list = set([inner for outer in movies.genres.str.split('|') for inner in o
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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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if st.button("Recommend!"):
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st.write(get_popular_recommendations(number_of_recommendations, genres_regex))
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import streamlit as st
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import pandas as pd
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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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# INSTRUCTIONS:
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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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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("Recommend!"):
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st.write(get_popular_recommendations(number_of_recommendations, genres_regex))
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pages/2 - User based recommender.py
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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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# 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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user_item_matrix = (
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ratings
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.merge(movies, on='movieId')[['title', 'rating', 'userId']]
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.pivot_table(index='userId', columns='title', values='rating')
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.fillna(0)
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)
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similarities_users = pd.DataFrame(cosine_similarity(user_item_matrix),
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index=user_item_matrix.index,
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columns=user_item_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 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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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_user_recommendations(user_id, n, genres):
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user_id = int(user_id)
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# calculate weights for ratings
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weights = similarities_users.loc[similarities_users.index != user_id, user_id] / sum(similarities_users.loc[similarities_users.index != user_id, user_id])
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# get unwatched movies for recommendations
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unwatched_movies = (
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user_item_matrix
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.loc[user_item_matrix.index != user_id, user_item_matrix.loc[user_id,:] == 0]
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.T
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)
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# compute weighted averages and return the n movies with the highest predicted ratings
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weighted_averages = pd.DataFrame(unwatched_movies.dot(weights), columns = ["predicted_rating"])
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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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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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# USER INPUT:
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user_id_input = st.text_input('User-ID')
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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("Recommend!"):
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st.write(get_user_recommendations(user_id_input, number_of_recommendations, genres_regex))
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requirements.txt
CHANGED
@@ -1,2 +1,3 @@
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streamlit
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pandas
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1 |
streamlit
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pandas
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scikit-learn
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