Providing Ad Links to Travel Blog Entries Based on Link Types
Aya ISHINO
Graduate School of Information Sciences, Hiroshima
City University,
Hiroshima, Japanishino@ls.info.hiroshima-cu.ac.jp

Hidetsugu NANBA
Graduate School of Information Sciences, Hiroshima
City University,
Hiroshima, Japannanba@hiroshimacu.ac.jp

Abstract
Content-targeted advertising systems
are becoming an increasingly importantpart of the funding for free web services.
These programs automatically find relevant keywords on a web page, and thendisplay ads based on those keywords.
We propose a method for providing
links to ads for travel products (which
we call ad links) automatically. We extract keywords from citing areas of
travel information links, and provide
appropriate ad links. To investigate theeffectiveness of our method, we conducted experiments. We obtained a highprecision for the extraction of keywords
and provision of ad links.

1

Introduction

Online advertising is a form of promotion thatuses the World Wide Web for the expressed purpose of delivering marketing messages to attract
customers. Examples of such online ads are contextual advertising and listing advertising. Content-targeted advertising systems, such as
Google’s Ad Sense program and Yahoo’s Contextual Match product, are becoming an increasingly important part of the funding for free webservices. These programs automatically find relevant keywords on a web page, and then display
ads based on those keywords. We propose amethod for providing ad links for travel products
to travel blog entries, which are travel journals
written by bloggers in diary form. By specifying
the travel domain, we aim to provide more appropriate ad links than existing content-targeted
advertising systems.

Toshiyuki TAKEZAWA
Graduate School of Information Sciences, Hiroshima
City University,
Hiroshima, Japantakezawa@hiroshimacu.ac.jp

To provide these ad links, we take account of
the types of hyperlinks in each travel blog entry.
Ishino et al. (2011) devised a method for constructing a collection of web links for travel information automatically. They extracted the hyperlinks by which bloggers describe useful web
sites for a tourist spot from travel blog entries,
and classified types of travel information link
into the following four categories.
S (Spot): The information is about tourist
spots.
H (Hotel): The information is about accommodation.
R (Restaurant): The information is about
restaurants.
O (Other): Other than types S, H, and R.

By switching strategies for providing ad links
according to these types, we attempt to provide
more appropriate ad links for travel.
The remainder of this paper is organized asfollows. Section 2 shows the system behavior interms of snapshots. Section 3 discusses related
work. Section 4 describes our methods. To investigate the effectiveness of our methods, we conducted some experiments, and Section 5 reportson these and the results. We present some conclusions in Section 6.

2

System Behavior

In this section, we describe our prototype system,
which provides information about (1) useful web
sites for tourist spots and (2) ads for travel products. The two steps in the search procedure are:
(Step 1) Input a keyword, such as “Okonomiyaki” (Japanese-style pancake), in the searchform (shown as ① in Figure 1).

(Step 2) Click the “link” button (shown as ②)
to generate a list of URLs for web sites related to
the keyword together with automatically identified link types using Ishino’s method (Ishino etal. 2011), the context of citations (“citing areas”),
by which the authors of the travel blog entries
describe the sites, and ads for travel products
related to the citing areas. We classified link
types into the four categories described above.
(Step 3) Click the “link”(shown as ③) to display detailed information of ads for travel products (shown in Figure 2).
We propose a method for providing ad links totravel products corresponding to the link type.

①②

Link type (R)Okonomiyaki (Japanese-style pancake)
URL

Citing areas③

Figure 1. A list of travel information links together with automatically inserted ad links

approach to ranking commercial products or various documents according to a user’s interests.
These systems can be classified into two categories by their underlying method of recommendation: (1) collaborative filtering (Goldberg et al.
1992) and (2) content-based filtering (Sarwar etal. 2000). We focus on the online advertising
using the content-based filtering techniques.
Examples of online advertising are listing advertising and contextual advertising. Listing advertising is a method of placing online ads on
web pages that show results from search engine
queries. Search advertisements are targeted tomatch the entered keywords. Fujita et al. (2010)
showed a system that automatically generates
shop-specific listing ads by reusing textual data
promoting each shop. They used a restaurant portal site as textual data. We use citing areas of
travel information links, and provide ad links torelated travel products.
In this paper, we focus on contextual advertising, which is based on keywords automaticallyextracted from the text of the web page. Keyword extraction is the core task of the contextual
advertising system. There are a number of classical approaches to extracting keywords. TF*IDF
uses a frequency criterion to select keywords.
Yih et al. (2006) showed a learning-based technique using TF*IDF for contextual advertising.
Recently, new methods based on the Wikipedia
corpus have been proposed. The Wikify! system(Mihalcea and Csmomai, 2007) identifies keywords in a text, and then links these keywords tothe corresponding Wikipedia pages. They identified keywords using a criterion called
keyphraseness. They applied their method toEnglish texts. We extract keywords from Japanese citing areas. Therefore, we cannot apply
their method for Japanese citing areas.

4

Automatic Organization of Travel Information through Blogs

The task of organizing travel through blogs isdivided into two steps: (1) classification of links
in travel blog entries, and (2) providing ad links
for travel information links. These steps are explained in Sections 4.1 and 4.2.
Figure 2. An example of a web page for a travel
product

3

Related WorkIn this section, we describe some related studies.
Recommendation systems provide a promising

4.1

Link Classification

Ishino et al. (2011) devised a method for classifying links in travel blog entries into the four
categories described in Section 1. The procedure
is as follows.

1.
2.

Input a travel blog entry.
Extract a hyperlink and any surrounding
sentences that mention the link (a citingarea).
3. Classify the link by taking account of theinformation in the citing area.
In the following, we will explain Steps 2 and 3.
Extraction of citing areas
Ishino et al. manually created rules for the automatic extraction of citing areas. These rules usecue phrases.
Method of link type classification
They classified hyperlinks automatically. They
employed a machine-learning technique using
the following features. Here, a sequence of nouns(a noun phrase) was treated as a noun.


A word.
Whether the word is a cue phrase, detailed as follows, where the numbers inbrackets shown for each feature represent
the number of cues (shown in Table 1,
Table 2, and Table 3).
Cue phrase

Number ofcues

A list of tourist spots, collectedfrom Wikipedia
Words frequently used in thenames of tourist spots, such as“ 動 物 園 ” (zoo) or “ 博 物 館 ”(museum)
Words related to sightseeing,such as “見学” (sightseeing) or“散策” (stroll)
Other words
Table 1. Cues for type S
Cue phrase

17,371

138

172
131

Number ofcues

Words frequently used in thename of hotels, such as “ホテル”(hotel) or “旅館” (Japanese inn)Component words for accommodation, such as “フロント” (frontdesk) or “客室” (guest room)
Words frequently used when
tourists stay in accommodation,such as “泊る” (stay) or “チェックイン” (check in)
Other words.
Table 2. Cues for type H

9

Cue phrase

Dish names such as “omelet”,
2,779
collected from Wikipedia
Cooking styles such as “Italiancuisine”, collected from Wikipe114
diaWords frequently used in thenames of restaurants, such as “レ21
ストラン” (restaurant) or “食堂”
(dining room)
Words used when taking meals,such as “食べる” (eat) or “おい52しい” (delicious).
General words that indicate food,31
such as “ご飯” (rice) or “料理”(cooking)
Other words
31
Table 3. Cues for type R
To investigate the effectiveness of their method, Ishino et al. conducted an experiment. The
evaluation results are shown in Table 4. We applied this model to 17,266 travel blog entries,
and classified 4,155 links. The numbers of automatically classified links are shown in Table 5.
We call these links travel information links. We
describe the method for providing ad links totravel products in the following section.
Link types
Recall (%)
Precision (%)
S
62.5
72.7
H
64.9
81.3
R
71.9
76.7
O
71.6
48.6
Table 4. Evaluation results for link classification (Ishino et al. 2011)
Link types
S
H
R
O
Number of
1,174
123
921
2,061
links
Table 5. The number of links of each type
4.2

29

14
21

Number ofcues

Providing Ad Links for Travel Information Links

The procedure for providing ad links for travel
information links is as follows.
1. Input a link type and the citing areas of a
travel information link.
2. Extract keywords from the citing areas.
3. Extract product data containing all keywords, and calculate the similarity be-tween the citing areas of a travel information link and the product data.
4. Provide an ad link to the product data
having the highest similarity for the travel
information link.
In the following, we will explain Steps 2 and 3.
Keyword extraction based on link types
We extract keywords for travel products corresponding to the link type. We use the cues usedby Ishino’s method for classifying travel information links, and extract keywords from citing
areas of link types S and R.
First, we describe the method for extracting
keywords from citing areas of link type S. The
cues for type S shown in Table 1, such as tourist
spots collected from Wikipedia and words frequently used in the names of tourist spots, tend to
become keywords. Therefore, we register thesecues as candidate keywords for link type S. If
citing areas of link type S contain candidate
keywords, we extract them as keywords. In addition, if citing areas contain names of places, we
extract them as keywords. Candidate keywords
for link type S are shown in Table 6.
Next, we describe a method for extracting
keywords from citing areas of link type R. Thecue for type R shown in Table 3, such as dishnames and cooking styles, tend to become keywords. Therefore, we register these cues as candidate keywords for link type R. If citing areas oflink type R contain candidate keywords, we extract them as keywords. Candidate keywords forlink type R are shown in Table 7. We used CaboCha
software(http://chasen.org/~taku/software/cabocha/)toidentify location names.

Number of

Candidate keywords

Dish names such as “omelet”,
collected from Wikipedia
Cooking styles such as “Italian
114
cuisine”, collected from Wikipedia
Table 7. Candidate keywords for link type R

Product data extraction based on the link types
We extract product data, which contain all keywords, and calculate the similarity between citing areas of a travel information link and the
product data.
Product data
We provide ad links to travel products of
Rakuten Shopping Mall (Rakuten Ichiba) or facility data of Rakuten Travel released throughthe Rakuten Institute of Technology for travel
information links. An example of product dataof Rakuten Ichiba is shown in Figure 3. The
product data contain 50 million items. An item
has a name, a code, a price, descriptive texts,
URL, picture, shop code, category ID and registration date. The facility data contain 11,468 facilities. A facility data entry has a name, ID
number, and user review.

Name
Code
Price
Descriptive text

Number of

Candidate keywords

candidate
keywords
17,812A list of tourist spots, collectedfrom Wikipedia
Words frequently used in the138
names of tourist spots, such as“動物園” (zoo) or “博物館”(museum)
Names of places
Table 6. Candidate keywords for link type S

candidate
keywords
2,779

URL
PictureCategory ID
Registration
date

Product data[marine diving] Earthly paradise
OKINAWA 2009seasir-umi:10001011
980*The guidebook contains extended information about diving in
Okinawa! *It introduces 140 divespots!
http://item.rakuten.co.jp/seasirumi/r023/@0_mall/seasirumi/cabinet/09shohin/rakuenokinawa_2009.jpg
101922
2010/03/24 15:08:58

Figure 3. Product data
We provide ad links to product data having associations with travel information links by using
the link types of the travel information links. We
take account of the characteristics of link types,
and assign each category to link types. Categories of product data are shown in Table 8.

Facility data do not contain descriptive texts
about the facilities. Therefore, we proposed amethod for providing ad links for travel information links of link types S and R.

Link types

Category

Number of
product data
items

Product data
related to trav51,516el.
H (Hotel)Facility data.
11,468
Product data
R (Restaurant)
830,807
related to food.
Table 8. Category of product data assigned to eachlink type.
S (Spot)areas of a travel information link and the productdata. Score is calculated as follows:
Score   Link _ Score(k i ) * Advertising _ Score(k i )Link_Score(ki) is the number of times the given keywords ki appears in that citing area. Advertising_Score(ki) is the number of times the given
keyword ki appears in the descriptive text of the
product data. We provide an ad link to the product having the highest score for the travel information link.

5

Experiments

To investigate the effectiveness of our methods,
we conducted several experiments.
5.1

We describe a method for collecting product data
related to travel. Product data has a category ID.
A category master, shown in Table 9, was released through the Rakuten Institute of Technology. The category master has a hierarchic structure. First, we collect subcategories of the category “Travel, Study abroad, Outdoor amusement”. In this way, we collect category IDs related to travel, and show the category IDs in Table
10. Next, we collect product data with category
ID related to travel as product data related totravel. In the same way, we collect product data
related to food.
Category name Subcategory ID
Travel,Study abroad,
101242
200162
Outdooramusement
209835
SANYO
209830503221
Panasonic
503218
Table 9. The category master
Category name
Hot spring
Theme park
Guidebook
Travel book, Travel essay
Mountain climbing,411387
Outdoor amusement, Camp
Table 10. Category IDs related to travel

Similarity calculation
We describe the method for calculating the similarity between citing areas and product data. We
extract product data that contain all keywords K,
and calculate the similarity Score between citing

Experimental Method

To generate the test data for providing ad links,
we randomly selected 50 travel information linksof type S and 50 of type R and manually classified them. To investigate the effectiveness of our
method, we extracted keywords using the following two methods for evaluation of keywords in
Section 5.2 and ad links in Section 5.3.



Category ID

Category ID
208922208924208925208930(1)

ki  K

Our method: Extract keywords by our
method, as described in Section 4.2.
TF*IDF: TF*IDF is a conventional baseline used in the scientific literature forcomparison of keywords extraction algorithms. IDF was calculated using an openAPI
(http://developer.yahoo.co.jp/webapi/sear
ch/websearch/v2/websearch.html).
We
extract the top-X words as keywords.

In addition, we evaluated our method for ad
links for the limited categories described in Section 4.2 and all categories of product data.
5.2

Evaluation of Keywords

We evaluated the method for keywords extraction. We used precision as the evaluation measure, calculated as follows:
Precision 

the number of appropriative keywords
the number of extravted keywords(2)

The evaluation results are shown in Table 11.
Our method attained higher precision than baseline methods.

Link Type S
Link Type R(%)(%)
Our method
81.8 (72/88)
98.0 (49/50)
TF*IDF(X=1) 46.0 (23/50)
48.0 (24/50)
TF*IDF(X=2) 38.0 (38/100) 37.4 (37/99)
TF*IDF(X=3) 36.0 (54/150) 36.5 (54/148)
TF*IDF(X=4) 34.0 (68/200) 31.5 (62/197)
TF*IDF(X=5) 34.0 (85/250) 32.5 (80/246)
Table 11.Precision of extracted keywords
There were two typical errors in keyword extraction: (1) the lack of cues and (2) a problem
with the method of keywords extraction. We describe these errors as follows.
(1) The lack of cues:
For keywords extraction, we used manually selected cues, as described in Section 4.2. To improve the coverage of cues, a statistical approach,such as applying n-gram statistics to a larger blog
corpus, will be required.
(2) The problem with the method of keywords
extraction:
In the following example, our method mistakenly extracted “天然温泉” (a natural hot spring)as a keyword, because the candidate keywords
for link type S “天然温泉” (a natural hot spring)appears in the citing areas.
[original]
お湯のヌルヌル感と 温 まり具合は最高です。
高浜の「湯っぷる」
詳しいことは→http://www.seaside-takahama.com/
ここは天然温泉じゃないんですが[translation]
The quality and the temperature of spring water
are great.
“YUPPLE” in Takahama.
For details, access the following web page:
http://www.seaside-takahama.com/
YUPPLE is not a natural hot spring.
5.3

Evaluation of Ad Links

We evaluated the method for providing ad links
for travel information links. We used precisionand coverage as evaluation measures, whichwere
calculated
asfollows:

the number of appropriative travel
information links provided ad links
Precision 
the number of travel information
links provided ad links(3)

the number of travel information links
Coverage provided ad links
the number of travel information links(4)

The evaluation results are shown in Table 12
and Table 13. We obtained a higher precision
than the baseline methods.
Precision
Coverage(%)(%)
Our method
79.3 (23/29)
58.0 (29/50)
TF*IDF(X=1) 39.1 (18/46)
92.0 (46/50)
TF*IDF(X=2) 37.8 (14/37)
74.0 (37/50)
TF*IDF(X=3) 35.9 (7/20)
40.0 (20/50)
TF*IDF(X=4) 36.4 (4/11)
22.0 (11/50)
TF*IDF(X=5)
0.0 (0/2)
4.0 (2/50)
Table 12. Precision and coverage of providing adlinks for travel information links of type S
Precision
Coverage(%)(%)
Our method
91.3 (21/23)
46.0 (23/50)
TF*IDF(X=1) 22.0 (11/50)
100.0 (50/50)
TF*IDF(X=2) 29.5 (13/44)
88.0 (44/50)
TF*IDF(X=3) 25.0 (7/28)
56.0 (28/50)
TF*IDF(X=4)
6.3 (1/16)
32.0 (16/50)
TF*IDF(X=5) 25.0 (2/8)
16.5 (8/50)
Table 13. Precision and coverage of providing adlinks for travel information links of type R
First, we discuss the results for link type S. As
shown in Table 12, our method attained higher
precision than the baseline methods. For coverage, TF*IDF (X=1) and TF*IDF (X=2) were
better than our method, while we obtained more
appropriate travel information links provided asad links than the baseline method.
Next, we discuss the results for link type R. As
shown in Table 13, our method attained higher
precision than the baseline methods. For coverage, TF*IDF (X=1), TF*IDF (X=2) and TF*IDF(X=3) were better than our method, while weobtained larger numbers of appropriate travel
information links provided as ad links than thebaseline methods. Therefore, our experimental
results confirm the effectiveness of our methods.
We discuss a typical error in providing links.
In the following example, we could not provide
ad links. Our method extracted an appropriative
keyword “Nihon-heso-kōen” (The Navel Park,

NISHIWAKI, JAPAN). Nihon-heso-kōen is apark in Hyogo prefecture, Japan. However, there
are no product data containing this word, and we
could not provide any ad links for the travel information link. To solve this problem, we took
the address of Nihon-heso-kōen from web pages,
and provided an ad link to a guidebook that describes tourist spots near Nihon-heso-kōen for
the travel information link.
[original]
今回初めてお会いした、Kamon さん奥さん
は、モデルさんのようなとても綺麗な方でした！
このメンバーで、第一目的地”日本へそ公園”へ
ここでの目的は、やっぱり”ぶーにゃん”に会う事・・・
が、今回も会えませんでしたつД｀)・゜・。・゜゜・*:.。
[translation]
I met with Mrs. Kamon for the first time. She
was very beautiful like a model!
We came to visit “Nihon-heso-kōen” (The Navel
Park, NISHIWAKI, JAPAN).
We expected to see “Bu-nyan”, but we missed
again :-(
5.4

Precision(%)

Coverage(%)

Our method(particular cate- 91.3 (21/23) 46.0 (23/50)
gory)
Baseline
70.8 (17/24) 48.0 (24/50)(all categories)
Table 15. Precision and coverage of ad links fortravel information links of type R (evaluation ofparticular category)
As shown in the tables, our method attained
higher precision than the baseline method. For
coverage, baselines of types S and R were better
than our method, while we obtained a larger
number of appropriate travel information links
provided as ad links than the baseline method.
Therefore, our experimental results confirm theeffectiveness of our methods.
As examples of more appropriate ad links thanour method could provide, the baseline method
could provide ad links to product data not directly related to travel, such as books, CDs andDVDs that describe tourist spots. These are notcategorized as product data related to travel inour system. In our future work, we plan to classify such product data as related to travel automatically so that we can provide more appropriate adlinks.

Evaluation of Limited Category

We evaluate our method for ad links for thelimited category shown in Section 4.2 and for all
categories of product data. We used precisionand coverage as evaluation measures, as described in Section 4.3. The evaluation results areshown in Table 14 and Table 15.
Precision(%)

Coverage(%)

Our method(particular cate- 79.3 (23/29) 58.0 (29/50)
gory)
Baseline
38.2 ( 13/34) 68.0 (34/50)(all categories)
Table 14. Precision and coverage of ad links fortravel information links of type S (evaluation particular category)6

Conclusion

We have proposed a method for providing adlinks for travel information links automatically.
For the extraction of keywords, we obtained
81.8% precision for link type S and 98.0% precision for link type R. For providing ad links, we
obtained 79.3% precision for link type S and78.3% precision for link type R. Our method also
provided larger numbers of appropriate travel
information links as ad links than the baseline
method. Our experimental results have confirmed the effectiveness of our methods.

7

Future WorkFor our method, we used manually selectedcues. To increase the number of cues, a statistical
approach is required.
In this paper, we have focused on travel information links written in Japanese. In our future
work, we will translate cue phrases from Japanese into other languages, and apply our method
to travel information links in various languages.

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