Online Advertising Networks - MongoDB Applied Design Patterns - page 146

def def choose_ads ( site_id , zone_id , user_id , keywords ):

site = db . ad . zone . find_one ({
'site_id' : site_id , 'zone_id' : zone_id })
iif site iis None : return return []

ads = ad_iterator ( site [ 'ads' ], keywords )
user = db . ad . user . find_one ({ 'user_id' : user_id })
iif user iis None : return return ads

for for ad iin ads :

advertiser_id = ad [ 'campaign_id' ] . split ( ':' , 1 )[ 0 ]
iif ad_is_acceptable ( ad , user [ advertiser_id ]):
yield yield ad

return return None

Our ad_iterator method has been modified to allow us to score ads based on both their
eCPM as well as their relevance:

def def ad_iterator ( ads , keywords ):

'''Find available ads, sorted by score, with random sort for ties'''
keywords = set ( keywords )
scored_ads = [
( ad_score ( ad , keywords ), ad ) for for ad iin ads ]

score_groups = groupby (
sorted ( scored_ads ), key = lambda

lambda score , ad : score )

for for score , ad_group iin score_groups :

ad_group = list ( ad_group )
shuffle ( ad_group )
for for ad iin ad_group : yield yield ad

def def ad_score ( ad , keywords ):

'''Compute a desirability score based on the ad eCPM and keywords'''
matching = set ( ad [ 'keywords' ]) . intersection ( keywords ) return return

ad [ 'ecpm' ] * math . log ( 1.1 + len ( matching ))

def def ad_is_acceptible ( ad , profile ):

# same as above

The main thing to note in the preceding code is that ads must now be sorted according to some
score , which in this case is computed based on a combination of the ecpm of the ad as well
as the number of keywords matched. More advanced use cases may boost the importance of
various keywords, but this goes beyond the scope of this use case. One thing to keep in mind
is that because the ads are now being sorted at display time, there may be performance issues
if a large number of ads are competing for the same display slot.