I'm Yutaro Ueoka from Tokyo Japan I'm a member of Yahoo Japan corporation
thank you for the opportunity to present our work.
Our presentation title is tackling cannibalization problems for online advertisement.
In this paper we did three points.
We defined multiple advertisement cannibalization, we name this MAC M A C
and we confirmed the MAC and we solved MAC by user modeling technique.
This is the content I will talk about.
In the first section I will introduce a cannibalization problem
and in the second section I will introduce online ad distribution system and the Minor category problem.
In the third section I will explain the boost minor model.
We developed the model to solve the minor category problem and as a result we found MAC.
In the fourth section I will explain subtract popular model and target silent model.
We developed these models to solve minor category problem and also MAC at the same time.
Then I will explain conventional cannibalization.
Some cannibalization had already been reported
for example offline-online cannibalization or search Ads Organic search results cannibalization
AOC is like this.
If search advertisements are inserted into the upper section or right section of Organic search results,
users click these advertisements instead of organic search results
and less likely to click Organic search results.
This means that search advertisements cannibalize organic search results.
So we call this problem cannibalization problem.
We found another type of cannibalization problem in this work.
2nd section is online ad distribution system.
Our advertisement distribution system has category targeting setting.
If advertisers had set the category targeting to their advertisements,
their advertisements are only delivered to users’ who are interested in the same categories.
For example Ad1 have a category target for insurance,
so this advertisement can only be delivered to User1
because interest categories of User1 contain Insurance.
But Ad1 cannot be delivered to User2 because interest categories of User2 does NOT contain insurance.
Of course, Advertisement categories are selected by advertisers.
However, users'interest categories are estimated by our system.
So, if we successfully estimate the interest categories for users, online ads are delivered more effectively.
Now, I want to describe the categories. This graph shows the diversity of categories.
There are 900 categories and
in this graph, vertical axis is the relative number of impressions
and the horizontal axis is a categories sorted by the number of impressions.
This graph is a long tail and we defined minor categories as lower 80 categories.
This minor categories have less than 1% of total clicks. Such minor category have problem.
There are major and minor category so minor categories are delivered less likely
and so minor category have less data and so this is a negative root and
so we have the motivation to enhance a minor category and eliminates the negative loop.
To solve the minor category problem, we developed BoostMinor model
To generate BoostMinor model, we prepared online questionnaires like this.
This questionnaire is for documentary category which is minor category.
Users answered to these questions. "Are you interested in documentary?" or
"Did you search the documentary?" or "Have you purchased documentary?"
This data become ground truth data.
We thought that the users' answers reflect their interests.
We did logistic regression to estimate users' interest from their search behavior.
By this model, we can predict other users' interest for minor category from their search behavior.
BoostMinor model is this logistic regression model, which we can add minor category to users interest category.
We test whether BoostMinor model work well or not by this experimental settings.
Here, I want to explain how to interpret this result.
target 80 means 80 minor category and others mean 820 non-minor category
and all means all 900 categories is equal to the target 80 plus other the category.
Percent means the gain of KPIs against baseline model.
Impression is a number of times ads had appeared in website.
iCTR stands for impression click-through rate and is calculated by the number of clicks divided by the number of impressions.
RPI stands for revenue per impression and is calculated as revenue divided by the number of impressions.
For example, we can interpret this red square part like this.
By using boost minor model, RPI of target 80 (minor category) was increased by 5.77% compared to the basline model.
By doing that we can evaluate BoostMinor model. target80 was strongly enhanced
This is good because BoostMinor model worked well.
But other categories were weakened and all category were also weaken
because depression of other category was too much.
this is bad result
We thought that Target80 cat. cannibalized Other cat. because users are finite.
We thought that multiple advertisement are cannibalized each others and so we thought MAC was confirmed.
To solve MAC and minor category problem at the same time, we developed these 2 models.
SubtractPopular model is a model that boost minor cat. only when a user is unlikely to click major category
For example, User X is interested in only category A.
Then X is less likely to click other major category ads, so we want to add HIGH score for category A for user X.
On the other side, User Y is interested in many categories. Then Y will click major category ads,
so we want to add LOW score for category A for user Y.
To realize the scoring, we calculate like this.
Vector A prime = Vector A for minor categories minus averaged major category vectors.
And we use these vectors to estimate users’ interest categories.
By this calculation, Users who are interested in major categories will not have minor interest category.
One more model we have developed is TargetSilent.
This model boost minor cat. only when users expressed a negative attitude toward ad clicking.
To find the negative attitude users, we used another questionnaire to classify users into 3 types.
Type 1 user like to click ads
Type 2 users do not have hesitation to click ads but ads are poorly matched.
Type 3 users dislike ads.
Type 3 users may dislike ads because they have never encountered to minor category advertisements.
so we want to target the type three users for minor category advertisement.
we group type one and type two users. these users have potential to click major ad.
So we want to calculate low score for A for users Type1 and 2.
On the other side, Type3 users will NOT click major ad.
So we want to try to deliver minor category ads. So we want to calculate high score for A for Type3 users.
To realize the scoring, we calculate the vectorA as vectorA for type 3 minus vectorA for type1 minus vectorA for type2.
This vector A can produce high score only for Type3 users.
The last model is Both model which combine subtractPopular model and TargetSilent model.
To evaluate these 3 models, we did experiment.
This is the results for 3 models. All models are compared to baseline model.
This BoostMinor model result is the result we have seen in the previous slide.
This is just for the reference to remember the previous results
And Target80 was increased but others was decreased too much, so total result was negative.
This is the previous result.
However, in SubtractPopular model and TargetSilent model,
Target80 categories were also enhanced and Others categories were slightly weaken
And this is the improved point that Others’ depression was so weak, that All cat. were still enhanced.
This results indicate that MAC was still existence
because Other categories ware weaken,
but total RPIs were enhanced and MAC was reduced
And this is the last result
In Both model, every group of categories group were enhanced.
This means that Both model can enhance minor categories without causing MAC
and results in the highest RPI gain compared to baseline model.
this is good.
thank you for listening