Recommender Systems

Recommender Systems

Collaborative Filtering Process

Challenge - Sparsity • Active users may have purchased well under 1% of the items (1% of 2 million books is 20,000 books). • Solution: Use sparse representations of the rating matrix.

Ratings in a hashtable 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},

Ratings in a hashtable '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},

Ratings in a hashtable 'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0, 'Superman Returns': 5.0, 'The Night Listener': 3.0, 'You, Me and Dupree': 3.5}, 'Toby': {'Snakes on a Plane': 4.5, 'Superman Returns': 4.0, 'You, Me and Dupree': 1.0} }

Finding Similar Users • Simple way to calculate a similarity score is to use Euclidean distance, which considers the items that people have ranked in common. People in preference space

Computing Euclidean Distance 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] if len(si) == 0: return 0 sum_of_squares = sum( [ (prefs[person1][item]-prefs[person2][item])**2 for item in si] ) return 1/(1+sqrt(sum_of_squares))

Pearson Correlation Score • The correlation coefficient is a measure of how well two sets of data fit on a straight line. Best fit line

Pearson Correlation Score • Corrects for grade inflation. • E.g., Jack Matthews tends to give higher scores than Lisa Rose, but the line still fits because they have relatively similar preferences. • Euclidean distance score will say they are quite dissimilar... Two critics with a high correlation score.

Pearson Correlation Formula

Geometric Interpretation • For centered data (i.e., data which have been shifted by the sample mean so as to have an average of zero), the correlation coefficient can also be viewed as the cosine of the angle between two vectors. E.g., • suppose a critic rated five movies by 1, 2, 3, 5, and 8, respectively, • and another critic rated those movies by .11, .12, .13, .15, and .18. • These data are perfectly correlated: y = 0.10 + 0.01 x. • Pearson correlation coefficient must therefore be exactly one. • Centering the data (shifting x by E(x) = 3.8 and y by E(y) = 0.138) yields x = (−2.8, −1.8, −0.8, 1.2, 4.2) and y = (−0.028, −0.018, −0.008, 0.012, 0.042), from which as expected.

Pearson Correlation Code def sim_pearson(prefs, person1, person2): si=[] for item in prefs[person1]: if item in prefs[person2]: si += [item] n = len(si) if n == 0: return 0 #Add up all the preferences sum1 = sum([prefs[person1][item] for item in si]) sum2 = sum([prefs[person2][item] for item in si]) #Sum up the squares sum1Sq = sum([prefs[person1][item]**2 for item in si]) sum2Sq = sum([prefs[person2][item]**2 for item in si]) #Sum up the products pSum=sum([ prefs[person1][item] * prefs[person2][item] for item in si ]) #Calculate Pearson Score numerator = pSum-(sum1*sum2/n) denumerator = sqrt( (sum1Sq-sum1**2/n) * (sum2Sq-sum2**2/n) ) if denumerator == 0: return 0 return numerator/denumerator

Top Matches def topMatches(critics, person, n=5, similarity=sim_pearson): scores=[ (similarity(critics,person,other), other) for other in critics if other!=person] scores.sort() scores.reverse() return scores[0:n] >> recommendations.topMatches(recommendations.critics,'Toby',n=3) [(0.99124070716192991, 'Lisa Rose'), (0.92447345164190486, 'Mick LaSalle'), (0.89340514744156474, 'Claudia Puig')]

Recommending Items

Recommending Items def getRecommendations(prefs, person, similarity=sim_pearson): totals={} simSums={} for other in prefs: if other==person: continue sim=similarity(prefs,person,other) if sim<=0: continue for item in prefs[other]: # only score movies I haven't seen yet if item not in prefs[person]: # Similarity * Score totals.setdefault(item,0) totals[item]+=prefs[other][item]*sim # Sum of similarities simSums.setdefault(item,0) simSums[item]+=sim

Recommending Items # 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 >>> recommendations.getRecommendations(recommendations.critics,'Toby') [(3.3477895267131013, 'The Night Listener'), (2.8325499182641614, 'Lady in the Water'), (2.5309807037655645, 'Just My Luck')]

Matching Products • Recall Amazon…

Transform the data {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5}, 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}} to: {'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0}, 'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc.. 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

Getting Similar Items >> movies=recommendations.transformPrefs(recommendations.critics) >> recommendations.topMatches(movies,'Superman Returns') [(0.657, 'You, Me and Dupree'), (0.487, 'Lady in the Water'), (0.111, 'Snakes on a Plane'), (-0.179, 'The Night Listener'), (-0.422, 'Just My Luck')]

Whom to invite to a premiere? >>recommendations.getRecommendations(movies,'Just My Luck') [(4.0, 'Michael Phillips'), (3.0, 'Jack Matthews')] • For another example, reversing the products with the people, as done here, would allow an online retailer to search for people who might buy certain products.

Building a Cache 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) for item in itemPrefs: # Find the most similar items to this one scores=topMatches(itemPrefs,item,n=n,similarity=sim_distance) result[item]=scores return result

Recommender Systems