Created
March 29, 2009 17:12
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# 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 |
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