recommendations.py

# A dictionary of movie critics and their ratings of a small 
# set of movies 
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 
  # Find the number of elements 
  n=len(si) 
  # if they are no ratings in common, return 0 
  if n==0: return 0 
  # Add up all the preferences 
  sum1=sum([prefs[p1][it] for it in si]) 
  sum2=sum([prefs[p2][it] for it in si]) 
  # Sum up 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 up the products 
  pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si]) 
  # Calculate 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] 
  # Sort the list so the highest scores appear at the top 
  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