基于用户的推荐系统曼哈顿算法代码,Python代码库,德仔网

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【Python】基于用户的推荐系统曼哈顿算法

作者:振祥 / 发布于2014/10/10/ 727


	#-*- coding: utf-8 -*-
	'''
	Created on 2012-9-3

	@author: Jekey
	'''
	import codecs
	from math import sqrt

	users = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0, "Norah Jones": 4.5, "Phoenix": 5.0, "Slightly Stoopid": 1.5, "The Strokes": 2.5, "Vampire Weekend": 2.0},
	 "Bill":{"Blues Traveler": 2.0, "Broken Bells": 3.5, "Deadmau5": 4.0, "Phoenix": 2.0, "Slightly Stoopid": 3.5, "Vampire Weekend": 3.0},
	 "Chan": {"Blues Traveler": 5.0, "Broken Bells": 1.0, "Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5, "Slightly Stoopid": 1.0},
	 "Dan": {"Blues Traveler": 3.0, "Broken Bells": 4.0, "Deadmau5": 4.5, "Phoenix": 3.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 2.0},
	 "Hailey": {"Broken Bells": 4.0, "Deadmau5": 1.0, "Norah Jones": 4.0, "The Strokes": 4.0, "Vampire Weekend": 1.0},
	 "Jordyn": {"Broken Bells": 4.5, "Deadmau5": 4.0, "Norah Jones": 5.0, "Phoenix": 5.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 4.0},
	 "Sam": {"Blues Traveler": 5.0, "Broken Bells": 2.0, "Norah Jones": 3.0, "Phoenix": 5.0, "Slightly Stoopid": 4.0, "The Strokes": 5.0},
	 "Veronica": {"Blues Traveler": 3.0, "Norah Jones": 5.0, "Phoenix": 4.0, "Slightly Stoopid": 2.5, "The Strokes": 3.0}
	 }

	#计算曼哈顿距离
	def manhattan(rate1,rate2):
	 distance = 0
	 commonRating = False
	 for key in rate1:
	 if key in rate2:
	 distance+=abs(rate1[key]-rate2[key])
	 commonRating=True
	 if commonRating:
	 return distance
	 else:
	 return -1

	#返回最近距离用户
	def computeNearestNeighbor(username,users):
	 distances = []
	 for key in users:
	 if key<>username:
	 distance = manhattan(users[username],users[key])
	 distances.append((distance,key))
	 distances.sort()
	 return distances
	#推荐
	def recommend(username,users):
	 #获得最近用户的name
	 nearest = computeNearestNeighbor(username,users)[0][1]
	 recommendations =[]
	 #得到最近用户的推荐列表
	 neighborRatings = users[nearest]
	 for key in neighborRatings:
	 if not key in users[username]:
	 recommendations.append((key,neighborRatings[key]))
	 recommendations.sort(key=lambda rat:rat[1], reverse=True)
	 return recommendations

	if __name__ == '__main__':
	 print recommend('Hailey', users)

试试其它关键字

　曼哈顿算法　　推荐系统　

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