< ICEC 2010, Aug. 3rd, 2010 >Automatic Generation of Listing Ads
by Reusing Promotional Texts
Atsushi FUJITA
Future University Hakodate, JAPANKatsuhio IKUSHIMA, Satoshi SATO
Nagoya University, JAPANRyo KAMITE, Ko ISHIYAMA, Osamu TAMACHI
Recruit Co., Ltd., JAPANSearchClick
Search Query

Results w/ Ads
 Generation of content-specific ads

Using information in the landing page
 Tailored for the restaurant domainLanding Page

2

Outline
1.
2.
3.
4.
5.

Background & Goal
Research Platform
Our Ad Generator
Evaluation
Conclusion Media-wise (cf. 2008) [Dentsu Inc., 10]

Specialized media
2,316B JPY (88.2%)
 TV programs
1,714B JPY (89.8%)
 Internet
707B JPY (101.2%)
 Newspapers
674B JPY (81.4%)
 Magazines
303B JPY (74.4%)
 Radio programs
137B JPY (88.4%)
 Fee for media

545B JPY (101.4%)
For PCs: 442B JPY (99.0%)


Keyword driven ads: 171B JPY (108.6%)

For Mobiles: 103B JPY (112.9%)
Keyword driven ads: 22B JPY (131.8%)

 Cost for creating ads

162B JPY (100.7%)

4 Textual ads

Search Query

Bid Phrase

グルメ

ホットペッパー グルメ

クーポン

クーポン

Bid Phrase
gourmetcouponHot Pepper
gourmetcoupon
 Titleif matched Descriptive Text
Title
ホットペッパーのクーポン
日本最大級のグルメサイトでお店探し Display URL
Descriptive Text
エリア、ジャンルで選べるお得なお店
www.hotpepper.jp
 Landing URL
Display URL


if clicked
Landing URL

 Pay-per-click (PPC) Free for just displayed Effective way of promotion5 Improve bid phrase

To make search engines show the ad more frequently Intensive studies on keyword suggestion[Joshi+, 06] [Abhishek+, 07] [Chen+, 08] [Ravi+, 10]

e.g., https://adwords.google.com/select/KeywordToolExternal

 Improve title and descriptive textTo make users to click on the ad more frequentlyRelevance and reliability [Jansen+, 05]

Only few worke.g., Ad Text Writer: http://adlab.microsoft.com/Ad-Text-Writer/ Not so attractive yet6 Develop an ad generator

To reduce human labor and other costs spent for promotion
 To recommend thousands of contents more effectively
Restaurant domain
 Evaluate the attractiveness of ads
 Click-through-rate (CTR) Focus: descriptive texts
Compare the information source Compare methodsOverture
Sponsored Search®

7Outline
1.
2.
3.
4.
5.

Background & Goal
Research Platform
Our Ad Generator
Evaluation
Conclusion Hot Pepper FooMoo (http://hotpepper.jp/)

Powered by Recruit Co., Ltd.
 Promoting 25,815 shops (October 2009)
Hot Pepper

Ads

Pay Per Click
Information
Informationand Couponand Fee(free paper & Web portal)
ServiceAdvertiser
Service charge

Ad ServiceClick

Ads
Consumer910 Each shop in special feature sites
Users’ typical search intentions
Search for the nearest branch of a particular franchise Search for shops that serve particular brands of liquor etc.


Extract the corresponding shops

Special feature site as a shortcut
Franchise name: Saint-Marc, Za Watami (a bar), Gyukaku (a grill shop)
 Shop type: grill, pub, sweets, bar Specialty: Japanese sake, curry, organ meat etc.
11

12

 Shop-specific info. for 25,815 shops (partly in-house)

General info.
 Advertising copy
 Photo captions
 Catch phrases
 etc.
 Info. of 200 special feature site

Feature name: yakiniku-jyouhou.net  Yakiniku
 Feature type (manually labeled): e.g., Yakiniku  Shop type Search query log
 483 thousand lines collected in the portal site
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Outline
1.
2.
3.
4.
5.

Background & Goal
Research Platform
Our Ad Generator
Evaluation
Conclusion I/O

Input: shop ID (in Hot Pepper) Output: Bid Phrases, Titles, and Descriptive Texts
 Other ad components
 Landing URL: URL of the target shop
 Display URL: www.hotpepper.jp
 Ad service assumed: Overture Sponsored Search®
 Length of title <= 15
 Length of descriptive text <= 33
 Prohibited characters (★，♪，etc.)
 etc.
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愛知の焼肉ならJ0000abcde

Shop IDAds

Bid Phrases

愛知 焼肉

1. Gen. Bid Phrases

Hot Pepper’s
Search Logs

Info of Special
Feature Sites

焼肉「XXX」YYY店。毎月ZZ日は全コース10%OFF。
www.hotpepper.jp

Titles

愛知の焼肉なら

2. Gen. Titles

Hot Pepper’s
Restaurant DB

List ofTrimming
Patterns

4. Compilation

3. Gen. Descriptive Texts

DescriptiveTexts

焼肉「XXX」YYY店。
毎月ZZ日は全コース10%OFF。
16

 Utilize findings in the previous studies

On the perception of users [Jansen+, 05] [Richardson+, 07] Domain-specific knowledge
Experiences of experts
 Users’ perception observed from search query

Bid Phrases

愛知 焼肉Titles

愛知の焼肉ならShop ID
J0000abcde

1. Gen. Bid Phrases

Hot Pepper’s
Search Logs

Info of SpecialFeature Sites

2. Gen. Titles

Hot Pepper’s
Restaurant DB17

 Place name AND shop genreXXX Sta.

People are looking for restaurants
To find a shop
 To get information about a specific shop

 Shop genre: feature name Place name: e.g., city name, station name, etc.

1. 3 types “area” fields of each shop
2. Apply 19 trimming patterns
e.g., “XXX (YYY・ZZZ)”  “XXX”, “YYY”, “ZZZ”

3. Filter out those have not queried at the Web portal
 Multiple candidates18

 By embedding the words of bid phrases into a template
Bid Phrase
愛知 焼肉Aichi Grill＋[place_name] の [feature_name] ならTitle


In case [feature_name] in [place_name]

愛知の焼肉なら
In case Grill in Aichi

To ensure the relevance to the queryTemplateBold-face when displayed

To satisfy the Overtures’s criteriaon the use of place/area names for bid phrases

19 Exploit shop-specific promotional texts (main focus)

Summarization-based approach Incorporate findings of the former researchPatterns, weights, etc.
Hot Pepper’s
Restaurant DB

List ofTrimmingPatterns

Shop ID
J0000abcde

3. Gen. Descriptive Texts

DescriptiveTexts

焼肉「XXX」YYY店。
毎月ZZ日は全コース10%OFF。

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 Key points

Characteristics of each shop
 Relevance to the users’ query Two-fold Shop name

焼肉「XXX」YYY店。黒毛和牛の飲放付コースが￥4515￥3000
Discounts for Japanese beef course with free drink.
焼肉「XXX」YYY店。全席掘りごたつだから楽ちんだね。
Comfortable with foot warmers set in floor recesses.
焼肉「XXX」YYY店。毎月29日は何かが起こる！
Something happens in 29th every month.

Extract from DB
 Trim to make shorter than 16 chars (one line)Shop-specific information(length <= (limit)-(length of shop name))
Method 1: sentence & phrase extraction
 Method 2: sentence trimming [Nomoto, 08]
 Method 3: sentence reconstruction [Oka+, 99]
21All 3 methods
 Basic text: advertising copy + α★満足な宴会は焼肉で決まり★
【焼肉】食べ放題＆【ドリンク】飲み放題＝クーポン利用で３０００・４０００円・５０００円がございます。
コラーゲン付！国産牛しゃぶしゃぶの【食べ放題】＆ドリンク【飲み放題】もクーポン利用で３５００円で楽しめます。
贅沢な食材で至福の時を満喫してください。 全席掘りごたつで、カップルはもちろん宴会にピッタリの空間をご用意！！
又、創業３０年、味自慢のXXXでは、カルビが絶対オススメ！！
是非、味・食感・香と３拍子揃ったうまい肉をお召し上がり下さい！！皆様の御来店を、スタッフ一同心よりお待ちしております(8 sentences) 6 other attributive elements

Method 1 only

Attributive element

Example of generated descriptive text

Access guide金山総合駅南口徒歩3分

Course names

飲み放題120分付き！ふぐ満載コース

Photo caption (recommended dishes)

名古屋風の甘辛手羽先。やみつきになるよ！

Photo caption (atmosphere)

奥の大広間は大人数の宴会にぴったり

Catch phrase of genre

旬にこだわるお寿司＆居酒屋Campaign types

20人以上の宴会が可能なお店22

 Procedure

Hot Pepper’s
Restaurant DB

1. Split text into sentences 2. Filter out long sentences 3. Select the best one


旬の食材をふんだんに使った料理が好評です。

旬の食材をふんだんに使った料理が好評です。
Dishes with plenty of seasonal foodstuff are popular here.

[Oka+, 99]
Score of relation(syntactic weight)
旬のScore of node 36(tf-idf)

20
食材を20ふんだんに303使った1132
料理が14好評です。
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Score of sentence = 2(36+20) + 3(20+11) + 0(30+11) + 3(11+14) + 2(14+24)
Score of edge23 Method 1 + candidate expansion at dependency tree level
Hot Pepper’s
Restaurant DB

Add new roots identified by ending patterns
旬の 食材を ふんだんに 使った 料理が 好評です。

旬の食材をふんだんに使った料理が好評です。

旬の 食材を ふんだんに 使った 料理が 好評
旬の食材をふんだんに旬の食材をふんだんに旬の食材をふんだんに使った料理が好評です。
使った料理が好評です。
使った料理が好評です。

Add all subtrees that contain the root node
旬の 食材を ふんだんに 使った 料理が 好評です。

旬の食材をふんだんに旬の食材をふんだんに旬の食材をふんだんに旬の食材をふんだんに使った料理が好評です。
旬の食材をふんだんに使った料理が好評です。
使った料理が好評です。
使った料理が好評です。
使った料理が好評です。

旬の食材をふんだんに使った料理が好評。

食材を ふんだんに 使った 料理が 好評です。
旬の 食材をDishes with plenty of seasonal foodstuff are popular here.

使った 料理が 好評です。
:24

 Grow a subtree greedily adjoining dependency edges
Hot Pepper’s
Restaurant DB

Obtain the set of dependency edges
旬の 食材を ふんだんに 使った 料理が 好評です。

旬の食材をふんだんに使った料理が好評です。

List of edges<食材を, 使った><旬の, 食材を><食材を, 使った><ふんだんに, 使った> <使った, 料理が><料理が, 好評です>

Greedily adjoin edges (in descending order of score)

Best edge List of remaining

食材を 使った

旬の食材を使った料理が食材を 使った 料理が旬の食材を使った料理。

旬の 食材を 使った 料理がedgesDishes with seasonal foodstuff.

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max 9N types
愛知の焼肉ならJ0000abcde

Shop ID

Ads

max N types
Bid Phrases

愛知 焼肉

1. Gen. Bid Phrases

Hot Pepper’s
Search Logs

Info of Special
Feature Sites

焼肉「XXX」YYY店。毎月ZZ日は全コース10%OFF。
www.hotpepper.jp

Titles

愛知の焼肉なら

2. Gen. Titles

Hot Pepper’s
Restaurant DB

List ofTrimming
Patterns

3. Gen. Descriptive Texts

DescriptiveTexts

4. Compilation

max 9 types

焼肉「XXX」YYY店。
毎月ZZ日は全コース10%OFF。
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Outline
1.
2.
3.
4.
5.

Background & Goal
Research Platform
Our Ad Generator
EvaluationConclusion 575 sampled shops, 2 place names  7,974 ads

2 bid phrases and 2 titles for all shops: OK Manual judgment of descriptive texts (autogen part)Ungrammatical ones
 Ones referring to outside context
 Measures
 Precision付き選べる鍋コース 4,780円最大
Selectable pot mal course with φ, ￥4780, up to φ
まずはこれを食べて欲しい。
We’d like you to eat this first# of appropriate descriptive texts / # of generated descriptive texts
 High precision  less human effort for screeningCoverage# of shops appropriate descriptive texts cover / # of target shops
 High coverage  less human effort for creating ads from scratch28

Equal to Table 7

78%+ Precision(92% in total)

Basic text covers
largest portion

38%+ Coverage(89% in total) Sufficiently high performance

6.8 appropriate descriptive texts per shop
 contribute to reducing workload But imperfect: need an effort for screening

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 105 sampled shops, 2 place names  1,418 ads
 CTR: # of clicks / # of impressions
 Ad service
Overture Sponsored Search®
 One-month: November 18th - December 26th, 2009

Pairwise evaluation with a baseline adDiversity of # of impressions
 One good ad is enough for promotion

Ads w/ low CTRs are culled out by ad service

Baseline: template-based 210 ads
105 shops * 2 pairs of bid phrase and title[feature_name] ナビでお店探し Only descriptive text differs

Find a shop using [feature_name] navigator30Equal to Table 9

Type 3 achieved
highest rate

The simplest method
led best results

 Pairwise comparison of CTRs

In total 39% of ads had higher CTRs than their baseline ad Even simple method led best results

Genre-specific ads (types 8 & 9) were comparable to types 131

Table 10: Number ratio of ads, groups, and shops that had ahigher CTR than that of baseline ad.
UnitAds
GroupsShops

N
1,418
210
105

n.s.
532 (39%)168 (80%)
100 (95%)

p < 0.10
77 (5%)
38 (18%)33 (31%)

Table 10

 More effective promotion

95% of shops
Table 10 showsthenumberratioofads,groups,andshopsfor
 80% of groups (pairs of shop and bid phrase)which at least one of the automatically generated ads had a CTR
higher than that of a0 . In total, 532 unique ads out of 1,418 (39%)
had a CTR higher than the corresponding baseline ad, and the difference was significant for 77 of them. Although the ratio of ads
was not particularly high, that for groups and shops was quite high32This means that our automatic generation approach using textualOutline
1.
2.
3.
4.
5.

Background & Goal
Research Platform
Our Ad Generator
Evaluation
Conclusion Automatic generation of listing ads
Ad generator uses promotional texts for a portal site on the Web

Bid phrase & Title: keywords and a templateAt least two types can be generated for every shop

Description: trimming and summarizing promotional texts
3 methods
 Be of use: 90%+ Precision, 89% Coverage, 6.8 types for a shop

Measurement of CTR

39% of ads had higher CTRs than the template-based baseline ads
Only descriptive texts are different
 5% had significantly higher CTR

95% of shops had higher CTRs by using shop-specific ads34

 Extend the target

All contents in the same domain Other domain (Recruit Co., Ltd. operates some) Improve the technology
 Linguistic analysis (partly done)
To improve current system
 To improve bid phrase and title also

Exploitation of consumer-generated media

WOM advertising (can advertisers face the risks?)

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