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| import numpy as np | |
| import pandas as pd | |
| import random | |
| from sklearn.feature_extraction.text import TfidfVectorizer #for TF-IDF | |
| from sklearn.metrics.pairwise import linear_kernel | |
| W_belongs_to_collection = 0.16 | |
| W_genres = 0.10 | |
| W_original_language = 0.01 | |
| W_title = 0.11 | |
| W_overview = 0.08 | |
| W_production_countries = 0.01 | |
| W_production_companies = 0.02 | |
| W_tagline = 0.10 | |
| W_keywords = 0.10 | |
| W_Director = 0.03 | |
| W_Writer = 0.02 | |
| W_Cast = 0.02 | |
| W_Top_Cast = 0.03 | |
| W_budget_categorized = 0.01 | |
| W_length = 0.02 | |
| W_average_vote_categorized = 0.08 | |
| W_count_vote_categorized = 0.07 | |
| W_era = 0.03 | |
| tfidf = TfidfVectorizer(stop_words='english') #defining tfidf model which removes additional words such as 'the', 'or', 'in' | |
| df_popular_popularity = pd.read_csv('df_popular_popularity.csv', low_memory = False) | |
| df_popular_WR_Q = pd.read_csv('df_popular_WR_Q.csv', low_memory = False) | |
| df_cbf_Q = pd.read_csv('df_cbf_Q.csv', low_memory = False) | |
| df_cbf_Q['belongs_to_collection'] = df_cbf_Q['belongs_to_collection'].fillna("") | |
| df_cbf_Q['overview'] = df_cbf_Q['overview'].fillna("") | |
| df_cbf_Q['spoken_languages'] = df_cbf_Q['spoken_languages'].fillna("") | |
| df_cbf_Q['tagline'] = df_cbf_Q['tagline'].fillna("") | |
| df_cbf_Q['Director'] = df_cbf_Q['Director'].fillna("") | |
| df_cbf_Q['Writer'] = df_cbf_Q['Writer'].fillna("") | |
| df_cbf1 = df_cbf_Q | |
| df_cbf2 = df_cbf_Q | |
| df_cbf_tfidf_belongs_to_collection = tfidf.fit_transform(df_cbf1['belongs_to_collection']) | |
| cosine_sim_belongs_to_collection = linear_kernel(df_cbf_tfidf_belongs_to_collection, df_cbf_tfidf_belongs_to_collection) | |
| df_cbf_tfidf_genres = tfidf.fit_transform(df_cbf1['genres']) | |
| cosine_sim_genres = linear_kernel(df_cbf_tfidf_genres, df_cbf_tfidf_genres) | |
| df_cbf_tfidf_original_language = tfidf.fit_transform(df_cbf1['original_language']) | |
| cosine_sim_original_language = linear_kernel(df_cbf_tfidf_original_language, df_cbf_tfidf_original_language) | |
| df_cbf_tfidf_title = tfidf.fit_transform(df_cbf1['title']) | |
| cosine_sim_title = linear_kernel(df_cbf_tfidf_title, df_cbf_tfidf_title) | |
| df_cbf_tfidf_overview = tfidf.fit_transform(df_cbf1['overview']) | |
| cosine_sim_overview = linear_kernel(df_cbf_tfidf_overview, df_cbf_tfidf_overview) | |
| df_cbf_tfidf_pruduction_countries = tfidf.fit_transform(df_cbf1['production_countries']) | |
| cosine_sim_pruduction_countries = linear_kernel(df_cbf_tfidf_pruduction_countries, df_cbf_tfidf_pruduction_countries) | |
| df_cbf_tfidf_pruduction_companies = tfidf.fit_transform(df_cbf1['production_companies']) | |
| cosine_sim_pruduction_companies = linear_kernel(df_cbf_tfidf_pruduction_companies, df_cbf_tfidf_pruduction_companies) | |
| df_cbf_tfidf_tagline = tfidf.fit_transform(df_cbf1['tagline']) | |
| cosine_sim_tagline = linear_kernel(df_cbf_tfidf_tagline, df_cbf_tfidf_tagline) | |
| df_cbf_tfidf_keywords = tfidf.fit_transform(df_cbf1['keywords']) | |
| cosine_sim_keywords = linear_kernel(df_cbf_tfidf_keywords, df_cbf_tfidf_keywords) | |
| df_cbf_tfidf_Director = tfidf.fit_transform(df_cbf1['Director']) | |
| cosine_sim_Director = linear_kernel(df_cbf_tfidf_Director, df_cbf_tfidf_Director) | |
| df_cbf_tfidf_Writer = tfidf.fit_transform(df_cbf1['Writer']) | |
| cosine_sim_Writer = linear_kernel(df_cbf_tfidf_Writer, df_cbf_tfidf_Writer) | |
| df_cbf_tfidf_Cast = tfidf.fit_transform(df_cbf1['Cast']) | |
| cosine_sim_Cast = linear_kernel(df_cbf_tfidf_Cast, df_cbf_tfidf_Cast) | |
| df_cbf_tfidf_Top_Cast = tfidf.fit_transform(df_cbf1['Top Cast']) | |
| cosine_sim_Top_Cast = linear_kernel(df_cbf_tfidf_Top_Cast, df_cbf_tfidf_Top_Cast) | |
| df_cbf_tfidf_budget_categorized = tfidf.fit_transform(df_cbf1['budget_categorized']) | |
| cosine_sim_budget_categorized = linear_kernel(df_cbf_tfidf_budget_categorized, df_cbf_tfidf_budget_categorized) | |
| df_cbf_tfidf_Length = tfidf.fit_transform(df_cbf1['Length']) | |
| cosine_sim_Length = linear_kernel(df_cbf_tfidf_Length, df_cbf_tfidf_Length) | |
| df_cbf_tfidf_average_vote_categorized = tfidf.fit_transform(df_cbf1['average_vote_categorized']) | |
| cosine_sim_average_vote_categorized = linear_kernel(df_cbf_tfidf_average_vote_categorized, df_cbf_tfidf_average_vote_categorized) | |
| df_cbf_tfidf_count_vote_categorized = tfidf.fit_transform(df_cbf1['count_vote_categorized']) | |
| cosine_sim_count_vote_categorized = linear_kernel(df_cbf_tfidf_count_vote_categorized, df_cbf_tfidf_count_vote_categorized) | |
| df_cbf_tfidf_era = tfidf.fit_transform(df_cbf1['era']) | |
| cosine_sim_era = linear_kernel(df_cbf_tfidf_era, df_cbf_tfidf_era) | |
| cosin_sim_final = np.multiply(cosine_sim_belongs_to_collection, W_belongs_to_collection) + np.multiply(cosine_sim_genres, W_genres) + np.multiply(cosine_sim_original_language, W_original_language) + np.multiply(cosine_sim_title, W_title) + np.multiply(cosine_sim_overview, W_overview) + np.multiply(cosine_sim_pruduction_countries, W_production_countries) + np.multiply(cosine_sim_pruduction_companies, W_production_companies) + np.multiply(cosine_sim_tagline, W_tagline) + np.multiply(cosine_sim_keywords, W_keywords) + np.multiply(cosine_sim_Director, W_Director) + np.multiply(cosine_sim_Writer, W_Writer) + np.multiply(cosine_sim_Cast, W_Cast) + np.multiply(cosine_sim_Top_Cast, W_Top_Cast) + np.multiply(cosine_sim_budget_categorized, W_budget_categorized) + np.multiply(cosine_sim_Length, W_length) + np.multiply(cosine_sim_average_vote_categorized, W_average_vote_categorized) + np.multiply(cosine_sim_count_vote_categorized, W_count_vote_categorized) + np.multiply(cosine_sim_era, W_era) | |
| df_cbf2_indices = pd.Series(df_cbf2.index, index=df_cbf2['title']) | |
| def final_recommender_hot_picks_now(Watched_movies_list): | |
| recommended_list = [] | |
| for i in range(10): | |
| recommended_list.append(df_popular_popularity.loc[i, 'title']) | |
| return recommended_list | |
| #recommend based on weighted ratings | |
| def final_recommender_hot_picks_of_all_time(Watched_movies_list): | |
| recommended_list = [] | |
| for i in range(10): | |
| recommended_list.append(df_popular_WR_Q.loc[i, 'title']) | |
| return recommended_list | |
| #recommend based on content based | |
| def final_recommender_for_you(Watched_movies_list): | |
| recommended_list = [] | |
| if len(Watched_movies_list) < 3: | |
| for i in range(10): | |
| recommended_list.append(df_popular_WR_Q.loc[i, 'title']) | |
| else: | |
| recently_watched = Watched_movies_list[-3:] | |
| for i in range(len(recently_watched)): | |
| y = df_cbf2_indices[recently_watched[i]] | |
| z = list(enumerate(cosin_sim_final[y])) | |
| z = sorted(z, key=lambda x: x[1], reverse=True) | |
| z = z[1:16] | |
| k = [i[0] for i in z] | |
| for j in k: | |
| recommended_list.append(df_cbf2.loc[j, 'title']) | |
| recommended_list = list(set(recommended_list)) | |
| random.shuffle(recommended_list) | |
| recommended_list = recommended_list[:15] | |
| return recommended_list | |