# 基于物品进行过滤：
# 首先把｛用户1｛物品A：得分，物品B：得分。。。｝｝转换为｛物品A｛用户1：得分，用户2：得分。。。｝｝
# 根据上面转化的表格，可以根据欧式距或者皮尔逊来计算出不同物体之间的相似度（具体计算是计算不同物体同一个用户的得分差值的平方和的根，
# 也可以根据皮尔逊）
# 最后可以根据某一个用户未评过分的物体根据用户评过分的物体*用户对评分过物体的评分 求和来计算
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critics = {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
'The Night Listener': 3.0},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 3.5},
'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
'The Night Listener': 4.5, 'Superman Returns': 4.0,
'You, Me and Dupree': 2.5},
'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 2.0},
'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'Toby': {'Snakes on a Plane': 4.5, 'You, Me and Dupree': 1.0, 'Superman Returns': 4.0}}
from math import sqrt
# Returns a distance-based similarity score for person1 and person2
def sim_distance(prefs, person1, person2):
# Get the list of shared_items
si = {}
for item in prefs[person1]:
if item in prefs[person2]: si[item] = 1
# if they have no ratings in common, return 0
if len(si) == 0: return 0
# Add up the squares of all the differences
sum_of_squares = sum([pow(prefs[person1][item] - prefs[person2][item], 2)
for item in prefs[person1] if item in prefs[person2]])
return 1 / (1 + sum_of_squares)
# Returns the Pearson correlation coefficient for p1 and p2
def sim_pearson(prefs, p1, p2):
# Get the list of mutually rated items
si = {}
for item in prefs[p1]:
if item in prefs[p2]: si[item] = 1
# if they are no ratings in common, return 0
if len(si) == 0: return 0
# Sum calculations
n = len(si)
# Sums of all the preferences
sum1 = sum([prefs[p1][it] for it in si])
sum2 = sum([prefs[p2][it] for it in si])
# Sums of the squares
sum1Sq = sum([pow(prefs[p1][it], 2) for it in si])
sum2Sq = sum([pow(prefs[p2][it], 2) for it in si])
# Sum of the products
pSum = sum([prefs[p1][it] * prefs[p2][it] for it in si])
# Calculate r (Pearson score)
num = pSum - (sum1 * sum2 / n)
den = sqrt((sum1Sq - pow(sum1, 2) / n) * (sum2Sq - pow(sum2, 2) / n))
if den == 0: return 0
r = num / den
return r
# Returns the best matches for person from the prefs dictionary.
# Number of results and similarity function are optional params.
def topMatches(prefs, person, n=5, similarity=sim_pearson):
scores = [(similarity(prefs, person, other), other)
for other in prefs if other != person]
scores.sort()
scores.reverse()
return scores[0:n]
# Gets recommendations for a person by using a weighted average
# of every other user's rankings
def getRecommendations(prefs, person, similarity=sim_pearson):
totals = {}
simSums = {}
for other in prefs:
# don't compare me to myself
if other == person: continue
sim = similarity(prefs, person, other)
# ignore scores of zero or lower
if sim <= 0: continue
for item in prefs[other]:
# only score movies I haven't seen yet
if item not in prefs[person] or prefs[person][item] == 0:
# Similarity * Score
totals.setdefault(item, 0)
totals[item] += prefs[other][item] * sim
# Sum of similarities
simSums.setdefault(item, 0)
simSums[item] += sim
# Create the normalized list
rankings = [(total / simSums[item], item) for item, total in totals.items()]
# Return the sorted list
rankings.sort()
rankings.reverse()
return rankings
def transformPrefs(prefs):
result = {}
for person in prefs:
for item in prefs[person]:
result.setdefault(item, {})
# Flip item and person
result[item][person] = prefs[person][item]
return result
def calculateSimilarItems(prefs, n=10):
# Create a dictionary of items showing which other items they
# are most similar to.
result = {}
# Invert the preference matrix to be item-centric
itemPrefs = transformPrefs(prefs)
c = 0
for item in itemPrefs:
# Status updates for large datasets
c += 1
if c % 100 == 0: print
"%d / %d" % (c, len(itemPrefs))
# Find the most similar items to this one
scores = topMatches(itemPrefs, item, n=n, similarity=sim_distance)
result[item] = scores
return result
def getRecommendedItems(prefs, itemMatch, user):
userRatings = prefs[user]
scores = {}
totalSim = {}
# Loop over items rated by this user
for (item, rating) in userRatings.items():
# Loop over items similar to this one
for (similarity, item2) in itemMatch[item]:
# Ignore if this user has already rated this item
if item2 in userRatings: continue
# Weighted sum of rating times similarity
scores.setdefault(item2, 0)
scores[item2] += similarity * rating
# Sum of all the similarities
totalSim.setdefault(item2, 0)
totalSim[item2] += similarity
# Divide each total score by total weighting to get an average
rankings = [(score / totalSim[item], item) for item, score in scores.items()]
# Return the rankings from highest to lowest
rankings.sort()
rankings.reverse()
return rankings
def loadMovieLens(path='/data/movielens'):
# Get movie titles
movies = {}
for line in open(path + '/u.item',encoding='iso-8859-15'):
(id, title) = line.split('|')[0:2]
movies[id] = title
# Load data
prefs = {}
for line in open(path + '/u.data'):
(user, movieid, rating, ts) = line.split('\t')
prefs.setdefault(user, {})
prefs[user][movies[movieid]] = float(rating)
return prefs
print(loadMovieLens('ml-100k')['87'])