推荐系统小记20150123

一.此前的数据集

movies.dat

1::Toy Story (1995)::Animation|Children's|Comedy
2::Jumanji (1995)::Adventure|Children's|Fantasy

...

ratings.dat

1::1193::5::978300760
1::661::3::978302109

...

users.dat 记录用户的属性，不需要

1::F::1::10::48067
2::M::56::16::70072

...

二.当前的含有信任矩阵的数据集，初始信任很稀疏，不包含项目名字

ratings_data.txt

1 100 4
1 101 5

...

trust_data.txt

 22605 42915 1
 22605 5052 1

 ...

三.Python代码，初步版本，试用数据集1

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,td,person,n=10,similarity=sim_pearson):
  #scores=[(similarity(prefs,person,other),other) 
  #                for other in prefs if other!=person]
  scores=[]
  for other in prefs:
    if other==person: continue
    if other in td[person]:
      sim=0.7*similarity(prefs,person,other) + 0.3*td[person][other]
    else:
      sim=0.7*similarity(prefs,person,other)
    scores.append((sim, other)) # save as tuple @shaojh
  scores.sort()
  scores.reverse()
  return scores[0:n]

# do sth to the trust metric
def trustPropagate(td):
  for p1 in td:
    for p2 in td:
      if td[p1][p2] == 1: continue
      for p3 in td:
        if td[p2][p3] == 1:
          td[p1][p3] = 0.5 # trust reduction O(n^3)

# Gets recommendations for a person by using a weighted average
# of every other user's rankings
def getRecommendations(prefs,td,person,similarity=sim_pearson):
  totals={}
  simSums={}

  # calculate person's everage rating
  personSum=0
  personEverage=0 
  for item in prefs[person]:
    personSum+=prefs[person][item]
  personEverage=personSum/len(prefs[person])
  print 'personEverage=',personEverage

  scores=topMatches(prefs, td, person)
  #for other in prefs: 
  for sim,other in scores:
    print '    ',sim,' ',other
    # don't compare me to myself
    #sim=similarity(prefs,person,other)

    # ignore scores of zero or lower
    #if sim<=0: continue

    # calculate other's everage rating
    otherSum=0
    otherEverage=0 
    for item in prefs[other]:
      otherSum+=prefs[other][item]
    otherEverage=otherSum/len(prefs[other])
    print otherEverage

    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]-otherEverage)*sim
        # Sum of similarities
        simSums.setdefault(item,0)
        simSums[item]+=sim

  # Create the normalized list
  rankings=[(personEverage+total/simSums[item],item) for item,total in totals.items()]

  # Return the sorted list
  rankings.sort()
  rankings.reverse()
  return rankings[0:10]

# Input data
def loadRatings(path='C:/data'):
  #Load data
  prefs={}
  for line in open(path+'/ratings_data.dat'):
    (user,movieid,rating)=line.split(' ')[0:3]
    #print type(rating),rating[0:1],user,movieid
    prefs.setdefault(user,{})
    prefs[user][movieid]=float(rating[0])
  return prefs

def loadTrustData(path='C:/data'):
  # Load data
  td={}
  for line in open(path+'/trust_data.dat'):
    (tmp,user1,user2,rating)=line.split(' ')[0:4]
    td.setdefault(user1,{})
    td[user1][user2]=float(rating[0])
  return td

def test(prefs):
  for i in prefs['1']:
    print i
  print len(prefs['1'])