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隱語義模型推薦

基于矩陣分解（SVD）的推薦

# 先計算歌曲被當前用戶播放量/用戶播放總量 當做分值 triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user', 'listen_count']].groupby('user').sum().reset_index() triplet_dataset_sub_song_merged_sum_df.rename(columns = {'listen_count':'total_listen_count'}, inplace = True) triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged, triplet_dataset_sub_song_merged_sum_df) triplet_dataset_sub_song_merged.head()

triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count'] / triplet_dataset_sub_song_merged['total_listen_count'] triplet_dataset_sub_song_merged[triplet_dataset_sub_song_merged.user == 'd6589314c0a9bcbca4fee0c93b14bc402363afea'][['user', 'song', 'listen_count', 'fractional_play_count']].head(8)

from scipy.sparse import coo_matrixsmall_set = triplet_dataset_sub_song_merged user_codes = small_set.user.drop_duplicates().reset_index() song_codes = small_set.song.drop_duplicates().reset_index() user_codes.rename(columns = {'index': 'user_index'}, inplace = True) song_codes.rename(columns = {'index': 'song_index'}, inplace = True) song_codes['so_index_value'] = list(song_codes.index) user_codes['us_index_value'] = list(user_codes.index) small_set = pd.merge(small_set, song_codes, how = 'left') small_set = pd.merge(small_set, user_codes, how = 'left') mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']] data_array = mat_candidate.fractional_play_count.values row_array = mat_candidate.us_index_value.values col_array = mat_candidate.so_index_value.valuesdata_sparse = coo_matrix((data_array, (row_array, col_array)), dtype = float)user_codes[user_codes.user == '2a2f776cbac6df64d6cb505e7e834e01684673b6']

import math as mt from scipy.sparse.linalg import * from scipy.sparse.linalg import svds from scipy.sparse import csc_matrixdef compute_svd(urm, K):U, s, Vt = svds(urm, K)dim = (len(s), len(s))S = np.zeros(dim, dtype = np.float32)for i in range(0, len(s)):S[i, i] = mt.sqrt(s[i])U = csc_matrix(U, dtype = np.float32)S = csc_matrix(S, dtype = np.float32)Vt = csc_matrix(Vt, dtype = np.float32)return U,S,Vtdef compute_estimated_matrix(urm, U, S, Vt, uTest, K, test):rightTerm = S * Vtmax_recommendation = 250estimatedRatings = np.zeros(shape=(MAX_UID, MAX_PID), dtype = np.float16)recomendRatings = np.zeros(shape=(MAX_UID, max_recommendation), dtype = np.float16)for userTest in uTest:prod = U[userTest, :] * rithtTermestimatedRatings[userTest, :] = prod.todense()recomendRatings[userTest, :] = (-estimatedRatings[userTest, :].argsort()[:max_recommendation]return recomendRatings K = 50 urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] U,S,Vt = compute_svd(urm, K)uTest = [4, 23] uTest_recommended_items = compute_estimated_matrix(urm,U,S,Vt,uTest,K,True)for user in uTest:print('Recommendation for user with user id {}'.format(user))rank_value = 1for i in uTest_recommended_items[user, 0:10]:song_details = small_set[small_set.so_index_value == i].drop_duplicates('so_index_value')[['title','artist_name']]print('The number {} recommended song in {} BY {}'.format(rank_value, list(song_details['title'])[0], list(song_details['artist_name'])[0]))rank_value += 1

Recommendation for user with user id 4
The number 1 recommended song in Fireflies BY Charttraxx Karaoke
The number 2 recommended song in Hey_ Soul Sister BY Train
The number 3 recommended song in Lucky (Album Version) BY Jason Mraz & Colbie Caillat
The number 4 recommended song in OMG BY Usher featuring will.i.am
The number 5 recommended song in Vanilla Twilight BY Owl City
The number 6 recommended song in Billionaire [feat. Bruno Mars] (Explicit Album Version) BY Travie McCoy
The number 7 recommended song in Crumpshit BY Philippe Rochard
The number 8 recommended song in Love Story BY Taylor Swift
The number 9 recommended song in Use Somebody BY Kings Of Leon
The number 10 recommended song in The Big Gundown BY The Prodigy

Recommendation for user with user id 23
The number 1 recommended song in Aber Dich Gibt’s Nur Einmal Für Mich BY Guildo Horn
The number 2 recommended song in Let’s Go [from ‘Salvation’] BY New Order
The number 3 recommended song in Crumpshit BY Philippe Rochard
The number 4 recommended song in Otherside (Album Version) BY Red Hot Chili Peppers
The number 5 recommended song in Yellow BY Coldplay
The number 6 recommended song in Bring Me To Life BY Evanescence
The number 7 recommended song in Luvstruck BY Southside Spinners
The number 8 recommended song in The Scientist BY Coldplay
The number 9 recommended song in Creep (Explicit) BY Radiohead
The number 10 recommended song in Sultans of El Sur BY The Mercury Program

總結

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