Quoka is one of Germany’s largest Classified Ad Sites considering the number of its ads and visitors. The site has almost 6 million ads and on average 10 million site visits per month and 90 million page views per month.

1. RE-CATEGORIZING 220 000 ADS IN 3
HOURS
Re-categorization and duplicate search at Quoka.de. Saving time and expenses by using
Slamby Classifier for re-categorization and duplicate search.
Slamby-Semantics Ltd.
CASE STUDY

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Summary
Quoka is one of Germany’s largest Classified Ad Sites considering the number of its
ads and visitors. The site has almost 6 million ads and on average 10 million site visits
per month and 90 million page views per month.
Visitors love the site, and they have been giving a lot of feedback. The closer inspection
of user feedback and the study of customer behaviour on Quoka revealed that the
category tree is in need of some restructuring. Quoka decided that adds need to be re-
categorized into a few categories.
The standard method for re-categorization is manual restructuring, but for example
working with 4 moderators the process could have taken 3 weeks for Quoka, not to
mention that it would have cost a serious amount of money.
Quoka decided to give Slamby a try, and started to use Slamby for re-categorization
and for duplicate ad search. Slamby was able to re-categorize the ads from the old
categories to the new ones, and find duplicate ads automatically.
The Problem
In summary: Ads need to be re-categorized.
Quoka has an exciting area of their category tree bearing the label Erotik. In this field
they had 4 leaf categories:
o Flirts & Fun
o SMS Kontakt Flirt & Fun

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o Seitensprung
o Schöne Sünden
Quoka has changed the category structure and wanted to reorganize all of the ads into
5 existing leaf categories which are the following:
o Er sucht Sie
o Sie sucht Ihn
o Er sucht Ihn
o Sie sucht Sie
o Paare
In the former category Erotik Quoka had 220 219 ads in 4 leaf categories. The task was
to regroup the ads of the 4 previous categories into the 5 existing ones. Indeed, it was an
interesting challenge. :) Imagine how moderators would have felt, if they had had to check
all the ads manually. :)
The standard method for re-categorization is the manual restructuring of the categories
by moderators. This means, that all ads must be re-read, and it needs to be decided into
which new category they should be moved.
For example, working with 4 moderators the process could have taken 3 weeks for
Quoka, not to mention that it would have cost a serious amount of money.

4. 3For more information please visit www.slamby.com
Slamby as a Solution
Slamby provides a language-independent automatic categorization solution for
Classified Ad Sites.
Quoka started to work with Slamby under the following conditions:
o The result has to be at least 90% accurate, because as a German company
they put a special emphasis on accuracy.
o Slamby’s categorization has to be faster than manual moderation.
o And last but not least, the process has to be also cheaper than manual
moderation.
Challenge: 90% precision
So the challenge Slamby faced was to beat manual moderation in speed, cost-
efficiency and accuracy. Be faster, cheaper, and more precise: ok, challenge accepted!
:)
How Did Slamby Solve the problem?
Train Slamby with Your data, and let Slamby do the magic.
Slamby is an intelligent, language-independent automatic categorization solution, which
learns from data of Classified Ad Sites. It learns the category tree and the ads belonging
to the categories. Based on this knowledge, just like a human, Slamby Classifier is able to
read and understand ads and decide which goes into which category. Simply by reading
the ad’s title and description. Very simple.

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So what did we do exactly?
We fed the ads from the 5 existing categories to Slamby. For this purpose Quoka
provided us with a database consisting of 92 398 ads. These ads were in the 5 categories
and Slamby could learn those ads.
After Slamby has learnt the new categories, it was able to re-categorize the ads from the
old categories to the new ones automatically. It takes the first ad from an old category,
reads the title and the description, understands it and moves it into the new category.
220 219 ads remained uncategorized in the old categories belonging to Erotik that Slamby
had to process.
Quality Measurement
At Slamby we provide the most accurate automatic categorization solution. In order to
assure high quality we measure the efficiency of Slamby Classifier. Every time before using
Slamby Classifier we conduct precise quality measurement to ensure the perfect
functioning of Slamby.
How do we conduct quality measurement?
First, when we receive a training dataset, we pick out 3 distinct datasets, and set them
aside. After the training process we take those 3 datasets singled out, and re-categorize
them. We compare these results (the original categories, and the new categories resulting
from the re-categorization). If Slamby Classifier gives us the same category successfully,
we consider its performance to be satisfying, but if it gives us another, we consider it to
be wrong (however, in several cases the category given by Slamby Classifier was more

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suitable than the original).
During the quality measurement procedure we create two kinds of output diagram. The
first one is the score – precision diagram. Every category recommendation gets a score
between 0 and 1. We summarize in a score table how accurate the score intervals are.
Below we can see the values measured at Quoka.
Scores higher than 0.1 can be considered as good categories. Ads with a score below
0.1 are most likely good, and the results can be acceptable.
We provide another diagram for the quality measurement procedure. The second
diagram is the completeness-precision diagram.

7. 6For more information please visit www.slamby.com
The quality measurement shows that Slamby Classifier gave the right category in 87% of
the time and in almost all remaining cases it chose the right category.
Duplicate Ad Search
Duplicate search was one of the key aspects of the job. The new and the old categories
worked parallel for a short time on Quoka. We were aware that there were duplicate ads
in the old and the new categories in their database. Doing the re-categorization without
duplicate filtering would have resulted duplicate ads in the same categories and in the
database.
Therefore, before re-categorization Slamby conducted a duplicate search and found 3
882 duplicate ads (which were identical word for word). So we set these ads aside, and
Quoka could decide what will happen to them (delete them, activate them again, etc.)
The duplicate search process – as a part of the service – took 3 minutes for Slamby
Classifier.

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Re-categorizing the Ads
Finally, we had a dataset with 216 337 uncategorized ads, waiting for categorization.
The re-categorization process took 2 hours for Slamby Classifier, after which Quoka had
to apply manual categorization in almost none of the cases.1
Integration and Usage
After we had trained it, we were able to offer Quoka a dedicated Slamby Classifier. With
Slamby Classifier Slamby has successfully met the challenge of re-categorization and
duplicate ad search. Slamby provided a simple CSV file.
This file included only the original ads without duplicates, and the automatic category
recommendation results in the following format.
AD ID AD Title AD
Description
Recommended
Category ID
Score
Quoka could use the CSV file to do the re-categorization in their database.
1 Almost, because Quoka had to categorize some ads manually, where the issue whether the ad was
posted by a male or a female was unsettled. Most of the time, a brief look at the images solved the
problem. And since Quoka had the category “Paare” (Couples) it was an easy way to save all the
unclear ads in that category.

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Automatic Decision Making, Threshold
Using the Score table above Quoka was able to set the threshold to a given level of
accuracy. They could decide from which score on they will accept the result of the re-
categorization process, and below which score they want to check the ads. They decided
to accept all the recommendations, and checked only an insignificant minority of the Ads,
where Slamby’s choice was uncertain, especially where the issue whether the ad was from
a male or female was unsettled. But in most cases a single look at the images solved the
problem.
In the case of Quoka, as the Score-Precision table shows, achieving 90% precision meant
using a 0.21 threshold; i.e. they automatically accepted 93% of the results, and manually
checked the remaining ones.
Results
The duplicate filtering and the whole re-categorization process took Slamby Classifier 3
hours. Slamby and Quoka could work together and achieved the following results
completely:
o They saved a lot time with automatic re-categorization;
o saved money due to the less amount of work and time of the moderators;
o got a well re-categorized database, which has become almost entirely
accurate;
o and it took merely 2 days.