Una introdución a las tecnologías y algoritmos de recomendación, filtrado basado en el contenido y filtrado colaborativo

1. Recommenders Bosco Curtu 07/03/2009 [email_address]

13. def get_recommendations (uid,prefs): totals = simSums = {} for other in prefs: if other==uid: continue # don't compare me to myself sim = self .distance_between_users(prefs[uid],prefs[other]) for item in prefs[other]: if item not in prefs[uid]: # only score events I haven't seen yet # Similarity * Score totals.setdefault(item, 0 ) totals[item] += prefs[other][item]*sim # Sum of similarities simSums.setdefault(item, 0 ) simSums[item] += 1 rankings=[(total/simSums[item],item) for item,total in totals.items( )] return rankings prefs = {} prefs[ 'user1' ] = { 'it1' : 10 , 'it2' : 2 , 'it3' : 8 , 'it4' : 10 } prefs[ 'user2' ] = { 'it1' : 1 , 'it2' : 9 , 'it3' : 0 , 'it5' : 7 } prefs[ 'user3' ] = { 'it1' : 10 , 'it2' : 4 , 'it6' : 9 , 'it7' : 10 } prefs[ 'user4' ] = { 'it1' : 8 , 'it6' : 0 , 'it7' : 5 } prefs[ 'user5' ] = { 'it1' : 5 , 'it2' : 3 , 'it5' : 8 , 'it6' : 10 } >>> get_recommendations('user1',prefs) ['it6':6.4, 'it7':9] >>> get_recommendations('user2',prefs) ['it6':5.2]