一个用不同语言实现的简单推荐引擎_Python_下载.zip

#!/usr/bin/env python # encoding: utf-8 """ movie_recommender.py Created by Thomas Cabrol on 2012-04-06. Copyright (c) 2012 __MyCompanyName__. All rights reserved. """ import csv import operator from math import sqrt, pow from operator import itemgetter from itertools import combinations from collections import namedtuple, defaultdict def movie_name(): lookup = defaultdict() for line in open('/Users/thomas/Documents/data/datasets/movielens/ml-100k/u.item'): record = line.strip().split('|') movie_id, movie_name = record[0], record[1] lookup[movie_id] = movie_name return lookup def ratings(): ''' Iterate the source file using a generator ''' MovieRating = namedtuple('MovieRating', 'user_id, movie_id, rating, timestamp') for record in map(MovieRating._make, csv.reader(open('/Users/thomas/Documents/data/datasets/movielens/ml-100k/u.data', 'rb'), delimiter='\t')): yield record # MovieRating(user_id='196', movie_id='242', rating='3', timestamp='881250949') def users_ratings(): ''' Grouping the ratings per user, used as an input before generating the corated movie pairs ''' u = defaultdict(dict) for line in ratings(): u[line.user_id][line.movie_id] = int(line.rating) users = defaultdict(dict) for k, v in u.iteritems(): users[k] = sorted(v.items()) return users # {941: {1: 5, 258: 4, 7: 4, 15: 4, 273: 3, 147: 4, 919: 5, 408: 5, 294: 4, 298: 5, ...}} def coratings(): ''' For each user, create the pair of corated movies using combination, then accumulate the base data used for the correlation coeff. by pair Returns a dict with each pair and its data ''' coratings = defaultdict(dict) for user, ratings in users_ratings().iteritems(): for pair in [zip(*corating) for corating in combinations(ratings, 2)]: #941 [[(1, 258), (5, 4)], [(1, 7), (5, 4)], [(1, 15), (5, 4)], [(1, 273), (5, 3)], .... movie_pair = pair[0] rating_pair = pair[1] if movie_pair not in coratings: coratings[movie_pair] = defaultdict(float) coratings[movie_pair]['N'] += 1.0 coratings[movie_pair]['ratingSum'] += rating_pair[0] coratings[movie_pair]['rating2Sum'] += rating_pair[1] coratings[movie_pair]['ratingSqSum'] += pow(rating_pair[0], 2) coratings[movie_pair]['rating2SqSum'] += pow(rating_pair[1], 2) coratings[movie_pair]['dotProductSum'] += rating_pair[0] * rating_pair[1] return coratings def recommendations(minimum_coratings=30): ''' Actually builds the dict with the correlation coeff. for each pair of movies, given that there is a sufficient number of coratings ''' rec = defaultdict(dict) for corating, data in coratings().iteritems(): movie_1 = corating[0] movie_2 = corating[1] if data['N'] >= minimum_coratings: num = data['N'] * data['dotProductSum'] - data['ratingSum'] * data['rating2Sum'] den = sqrt(data['N'] * data['ratingSqSum'] - data['ratingSum'] * data['ratingSum']) * sqrt(data['N'] * data['rating2SqSum'] - data['rating2Sum'] * data['rating2Sum']) rec[movie_1][movie_2] = num/den rec[movie_2][movie_1] = num/den return rec def show_recommendations(top_n=10): ''' Print the top n recommendations for each movie ''' lu = movie_name() for movie, correlations in recommendations().iteritems(): #print movie, [e for i, e in enumerate(sorted(correlations.items(), key=itemgetter(1), reverse=True)) if i <= top_n-1] for i, related_movies in enumerate(sorted(correlations.items(), key=itemgetter(1), reverse=True)): if i == top_n: print break print movie, lu[movie], i+1, related_movies[0], lu[related_movies[0]], related_movies[1] ''' 50 Star Wars (1977) 1 172 Empire Strikes Back, The (1980) 0.747981422379 50 Star Wars (1977) 2 181 Return of the Jedi (1983) 0.672555855888 50 Star Wars (1977) 3 174 Raiders of the Lost Ark (1981) 0.536117101373 50 Star Wars (1977) 4 1142 When We Were Kings (1996) 0.515164066819 50 Star Wars (1977) 5 963 Some Folks Call It a Sling Blade (1993) 0.509016159525 ... ''' if __name__ == '__main__': show_recommendations()