基于用户的推荐系统曼哈顿算法,推荐曼哈顿算法,#-*- coding:
#-*- coding: utf-8 -*-'''Created on 2012-9-3@author: Jekey'''import codecsfrom math import sqrtusers = {"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 recommendationsif __name__ == '__main__': print recommend('Hailey', users)#该片段来自于http://byrx.net
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