recommendationengine.py

from math import sqrt
from imp import reload



# 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


def loadMovieLens():
# Get movie titles
    movies={}
    path='/data/movielens'
    #path is the address of movielens dataset
    for line in open(path+'/u.item','r'):
        (id,title)=line.split('|')[0:2]
        movies[id]=title
    # Load data
    prefs={}
    for line in open(path+'/u.data','r'):
        (user,movieid,rating,ts)=line.split('\t')
        prefs.setdefault(user,{})
        prefs[user][movies[movieid]]=float(rating)
    return prefs


# 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]

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


# 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 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 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

#movies=transformPrefs(critics)

prefs=loadMovieLens( )
prefs['56']
itemsim=calculateSimilarItems(prefs,n=50)

print(getRecommendedItems(prefs,itemsim,'56')[0:30])
#The above statement will print all movies similar to the 56th movie in the dataset.