Python用户推荐系统曼哈顿算法实现

转载http://www.iplaypy.com/code/algorithm/a2065.html

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}         }# Python计算曼哈顿距离 www.iplaypy.comdef 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# python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(key=lambda x: x[0], reverse=False)    return distances# 推荐python实现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__':    recommendation = recommend('Hailey', users)    print('recommend to Hailey:', recommendation)#recommend to Hailey: [('Phoenix', 4.0), ('Blues Traveler', 3.0), ('Slightly Stoopid', 2.5)]