A Competitive Information Recommendation
System and Its Behavior
Yasuhiko Kitamura1 , Toshiki Sakamoto1, and Shoji Tatsumi1
Osaka City University, Osaka 558-8585, Japan{kitamura,sakamoto,tatsumi}@kdel.info.eng.osaka-cu.ac.jp
http://www.kdel.info.eng.osaka-cu.ac.jp/

Abstract. Information recommendation systems draw attention of practitioners in B-to-C electronic commerce. In an independent recommendation system such as in www.amazon.com, a user cannot compare therecommended item with ones from other information sources. In a brokermediated recommendation system such as in www.dealtime.com, the broker takes the initiative of recommendation, and the information provider
cannot recommend its item directly to the user.
In this paper, we propose a competitive information recommendation
system consisting of multiple animated agents that recommend theiritems competitively, and discuss the advantages through showing a prototype developed for restaurant recommendation. Each agent recommends
restaurants from its own point of view and the user tells good or bad
about them. In our competitive information recommendation system,
the user can compare items recommended from multiple agents, and theinformation providers can recommend their items directly to the user
through its animated agent. We also show that the competitive natureaﬀects the output depending on the number of participating agents.

1

Introduction

Electronic Commerce (EC) is one of most successful application domains of theInternet. We can access a large number of shopping sites that deal with various
goods through a Web browser. A simplest and easiest way of starting a shoppingsite is just to create a Web page on which items for sales are listed up. When
there are too many items to be contained in plain Web pages, we may deploy an
information retrieval tool to help customers ﬁnd their preferred items.
Recently, to make the shopping sites more attractive, new technologies areadded to them. Recommender systems initiatively recommend items to customers by using collaborative and/or content-based ﬁltering techniques [2]. They
try to reduce customers’ burden of searching for their preferred items. Life-likeanimated agents, such as developed by Extempo (www.extempo.com), are alsoavailable to navigate customers to their preferred items in the site. They canbuild a close and friendly relationship between the shop and the customers.
Most of conventional shopping sites, such as amazon.com, are running in an
independent and closed manner. In such a site, customers receive information

for sales items from the site only and any devices for comparing the items withthose from the other sites are not facilitated.
To make the comparison easy, a number of comparison shopping sites, such as
Bargain Finder, Jango[3], DealTime (www.dealtime.com) and so on, have beendeveloped. However, such a site is run by a third party, which is independent
from buyers and sellers, and the design of how to compare and what (attribute)
to compare depends on the third party. It is told that many owners of shoppingsite are not happy with such comparison services because they just raise pricecompetitions ignoring the other additional services oﬀered by the sites.
In this paper, we propose a new multiagent based platform for EC wheremultiple shopping sites or information recommendation sites are integrated ina ﬂexible and interactive manner. The platform provides a virtual space wheremultiple animated agents, each of which is delivered from an information site,
interact with each other and the user to recommend items in a competitive
manner.
In this platform, the customer can compare recommended items with thoseby other agents and ﬁnd a preferred one by watching competitive recommendations performed by multiple agents on a browser. Through interactions with thecustomer, agents can learn his/her preference and use it for further recommendations. From a viewpoint of shopping site, this platform provides a virtually open
market place where agents can interact with the customer, and the agents can directly recommend items to the customer without intervention of an information
broker such as a comparison shopping site.
In Section 2, we show a prototype of competitive information recommendation system called Recommendation Battlers and how multiple animated agentsrecommend items in a competitive manner. In Section 3, we discuss the systemarchitecture of Recommendation Battlers and in Section 4, we discuss the rational recommendation method employed in the system. In Section 5, we show
a macroscopic behavior of Recommendation Battlers through simulation experiments and show how both utilities of agents and the user change depending on
the number of agents and the number of items. Finally, we discuss related works
and future work in Sections 6 and 7 respectively and summarize this paper in
Section 8.

2

Recommendation Battlers: A Prototype forCompetitive Restaurant Recommendation

Recommendation Battlers is a prototype of competitive information recommendation system. As shown in Fig. 1, two restaurant recommendation agents; Peedy
and Genie appear on a Web browser. Responding to a request from the user suchas “I would like to ﬁnd a good Indian restaurant near Shinsaibashi station,”
Peedy starts to gather relevant restaurant pages from Yahoo! Gourmet1 and extracts their addresses and average cost. It then calculate the distance from the1

http://gourmet.yahoo.co.jp/

nearest station by referring to the addresses. Genie also do the same process but
it uses another information source called Kansai Walkerplus2.
After gathering restaurant information from Web sites, Peedy and Genie
start to recommend restaurants in a competitive manner. A typical dialogue isgiven below.
(1)User: “I would like to ﬁnd a good Indian restaurant near Shinsaibashi station.”(2)Genie: (After gathering information from the Kansai Walkerplus) “Do you
like Restaurant A? The average cost is around 2,000 Yen and the distancefrom Shinsaibashi station is 90m.”(3)User: “Good.”(4)Peedy: (After gathering information from the Yahoo! Gourmet) “Do you
like Restaurant B? The average cost is around 2,500 Yen and the distancefrom Shinsaibashi station is 80m.”(5)User: “No good.”(6)Peedy: “Do you like Restaurant C? The average cost is around 2,200 Yen
and the distance from Shinsaibashi station is 80m.”(7)User: “Good.”(8)Genie: “I am sure that you like Restaurant D. The average cost is around
1,500 Yen and the distance from Shinsaibashi station is 70m.”(9)Peedy: “I have no more restaurant to recommend.”
The recommendation is performed by two character agents interacting witheach other and the user. An agent recommends a restaurant not only by showing
the Web page that contains the restaurant information, but also by telling comments about the average cost and the distance from the nearest station. Whenneeded, it asks whether the user likes the restaurant or not by showing a dialogue box. The user responds to the question by telling “Good” or “No good.”
Referring to the responses, the agents try to learn the user’s preference about
the cost and the distance and use it for further recommendations. When eitherof agents has no more restaurant to recommend, the process terminates.
The order of recommendation depends on the agent’s strategy. For example,
in the above dialogue, Peedy recommends Restaurant B before Restaurant C although Restaurant C apparently looks better than Restaurant B from the viewpoint of the average cost. In this case, Restaurant B is more beneﬁcial for Peedy
because we assume the agent receives more fee from the information provider
when it succeeds to broker the restaurant. In this sense, agents in Recommendation Battlers are not cooperative but are competitive. In Section 5, we analyze
the macroscopic behavior of agents and show how both utilities of agents andthe user change depending on the number of agents and the number of items.
However, in any way, agents should recommend restaurants in a rational mannersuch as recommending items in decreasing price order. We discuss more about
a rational recommendation method we employ in Section 4.
Recommendation Battlers has the following advantages.
2

http://www.walkerplus.com/kansai/gourmet/

Fig. 1. A snapshot of Recommendation Battlers. Peedy on the left shows a Web pageof an Indian restaurant from YAHOO! Gourmet and says “Do you like Restaurant
Gautama? The average cost is around 800 Yen and the distance from Shinsaibashi
station is 170m.” Genie on the right also shows a Web page of another restaurant called“New Light” from Kansai.Walkerplus.com and the user compare the two restaurants.

– It provides a platform where multiple information sources are integrated in
an interactive manner. By preparing another character agent, we can add
another information source into this platform.
– It provides a virtual space where multiple animated agents can recommend
items in an active and interactive manner. They are just like sales persons
who come from diﬀerent companies. Each sales person talks about his/hergoods from his/her own viewpoint. By comparing the talks, we can understand the advantage and disadvantage of goods more than just by hearing
from one.
– It provides a space shared among agents. A response from the user to an item
recommended by an agent helps other agents learn the user’s preference.
– It provides a friendly and easy-to-use interface to the user. Animated agentsrecommend items to the user in a friendly and active manner, and the user
just watches the process of recommendation performed on a browser. Even
when requested a response, he/she just does it by telling “Good” or “Nogood.”

3

System Architecture

Recommendation Battlers consists of multiple agents, a recommendation blackboard, and a browser with animated characters, as shown in Fig. 2.

Recommendation Blackboard

BrowserModule to
Decide Item

Item
Database
Item
Database

Fig. 2. System Architecture of Recommendation Battlers

Web

Module to Collect
Information
Information
Gathering
ModuleRecommendation
ModuleControl Program
Interface Module
Program

User

Agent
Agent

3.1

Agent

Agents play the most important role to gather information from WWW information sources and to recommend items through the character interface. An
agent consists of an information gathering module, an item database, a recommendation module, and an interface module.
Collecting Information The information gathering module gathers information from a WWW information source by using an information extraction wrapper. The information extraction wrapper consists of MetaCommander[4] andTemplate Filter.
MetaCommander is a tool to download Web pages by interpreting a given
script. It downloads not only simple Web pages designated by URLs but also
those that are accessible through the CGI. This module automatically translates
a request from the user into a MetaCommander script depending on the target
Web site.
For example, let us assume the user submits a request such as “I would liketo ﬁnd a good Indian restaurant near Shinsaibashi station.” The module ﬁrst
extracts keywords “Indian” (a recipe name) and “Shinsaibashi” (a place namein Osaka). Currently we just use a simple keyword matching method between
keywords in the user’s request and those in the tables of recipe and place names,
so the system may misinterpret a request such as “I want to ﬁnd an Italianrestaurant in the Indian quarter.”
An example of MetaCommander script is shown as follows.

getURL( "http://gourmet.yahoo.co.jp/bin/g_searcho","a1" = "15" ,"a2" = "270004" ,"oc" = "0" ,"jl" = "0205" ) {file( "yahoo/searchResult.html" ) {
print
}
}

getURL is a MetaCommander command to get a Web page through the CGI.
The ﬁrst parameter is the URL of Web page and the other parameters are
optional for the CGI depending on the Web site. In this case, “a1=15” means
Osaka, “a2=270004” means Shinsaibashi, and “jl=0205” means Indian food. The
information gathering module has a table that translates a recipe or place nameinto a site-dependent code such as “15” for Osaka, “0205” for Indian food, and soon. The command file is to specify the name of ﬁle that stores the downloaded
Web page.

Template Filter is a tool to extract the designated data by ﬁltering the datafrom a Web page through a template. Many of Web-based information sites
provide information in a ﬁxed format in HTML, so our Template Filter works
eﬀectively for such a site.
Gathered items are stored in the Item Database as a set of records in the
XML format as below.
<?xml version="1.0" ?><restaurantData><name>Gautama</name><recipe>Indian</recipe><address>1-18-2 Higashi Shinsaibashi, Chuo-ku, Osaka </address><budget>700--800 Yen (Lunch) <br /> 1100--1500 Yen (Dinner) </budget><url>www.gautama.co.jp</url></restaurantData>
Currently, we use a very rigid method for wrapping Web information sources.
Depending on the target Web source, we have to write a program by using MetaCommander and Template Filter. We, of course, can reduce the programmingtask by using these tools much more than by just using a naive programming
language such as C or Java.
Recommending Items Each agent controls the Web browser to display a Web
page as a recommendation to the user. At the same time, it also controls thecharacter to utter a comment about the recommendation. When it needs to geta feedback on the recommendation from the user, the character asks a question
to the user through a dialogue box. By using a character, we can recommend
items in an interactive and friendly manner.
In Recommendation Battlers, we use Microsoft Internet Explorer for thebrowser and Microsoft Agent as the character interface as shown in Fig. 1. The
detail of recommendation method is discussed in Section 4.
3.2

Recommendation Blackboard

The recommendation blackboard is used to share information among agents.
Recommendation blackboard shares the following information.
Request from the user: It keeps requests for recommendation from the user.
For example, when the user submits a request “I would like to ﬁnd a good Indian restaurant near Shinsaibashi station,” a record <request> <location>
Shinsaibashi </location> <recipe> Indian </recipe> </request> isstored in the blackboard.
Recommended items: It keeps information on items recommended by agentsas a set of records consisting of the recommended item, the list of their
attributes and values, and the agent that recommended the item.

Feedback from the user: It keeps feedbacks on recommended items from theuser as a set of records consisting of the recommended item and the feedback
from the user. The feedback is given by “Good” or “No good.”

4

Rational Recommendation

In Recommendation Battlers, multiple agents recommend items in a competitive
manner, but also they need to do it in an rational order. For example, when theuser has accepted a restaurant whose cost is 2,000 Yen and the distance from thenearest station is 150m, he/she is unlikely to accept another candidate whose
cost is 3,000 Yen and the distance is 300m. Agents need to recommend items ina way that the user likely to accept, and we propose a rational recommendation
method.
The process of information recommendation is as follows.
1. A request from the user is stored on the recommendation blackboard.
2. Each agent accesses the recommendation blackboard to get the request.
3. Each agent gathers information that meets the request from its own Web
information source.
4. An agent proposes an item to the user. If there is no agent that has appropriate items to propose, then the ﬂow terminates.
5. The user gives a feedback to the recommended item when needed.
6. Each agent accesses the recommendation blackboard to get the feedback
from the user.
7. Go to step 4.
At the step 4 of the above procedure, each agent monitors the other agent’s
proposals and the user’s responses. It then proposes a new item that is more
appropriate given the course of dialogue. We here use a rational recommendation
method to make agents propose appropriate items.
To simplify the discussion, we make the following assumptions.
– Each of item targeted has only two attributes (cost and distance). We associate item p with cost c and distance d using the tuple < p, c, d >– The lower an attribute value is, the better it is for the user.
– The user can respond to any proposal using either “Good” or “No good.”
Our method categorizes items into three groups: R, I, U .
R: A group of items that the user is likely to accept.
I: A group of items that the user is likely not to accept.
U : A group of items for which it is unknown whether they would be acceptedor not by the user.
Items placed in R are those whose attribute values are better than those of
the items already accepted. On the other hand, items placed in I are those whoseattribute values are worse than those of the items already accepted. Items that
do not fall into R or in I are placed into category U . For example, let us assume
that only one item < p1 , 2000, 150 > has been accepted. It follows that item
< p2 , 1500, 100 > would be placed into R because its attribute values are better
than those of p1 . Item < p3 , 3000, 200 > is placed in I because its attributevalues are worse than those of p1 . Item < p4 , 1500, 200 > and < p5 , 2500, 100 >are placed in U because it is unknown which attribute is more important to theuser at this time.
It is obvious that items in I should not be proposed to the user. On theother hand, items in R and U could be of interest to the user. For an item in
U , it is unclear if the user will accept it or not, so the agent needs to ask for
conﬁrmation, as in utterances (2), (4), and (6) in Section 2. It is highly likelythat the user will accept an item in R, so the agent does not need to ask for
conﬁrmation as in utterance (8) in mentioned Section 2.
In the rational recommendation method, the three groups, R, I, and U , must
be updated according to the last proposal and the response from the user. We
here explain how to update the groups.
Proposal from R Let us assume that < C, 2200, 80 > has been accepted andone agent proposes < D, 1500, 70 >. As D is in R, the agent does not need toseek the user’s conﬁrmation. When the agent proposes D, group boundaries are
updated as shown in Fig. 3. Items in region (x > 1500) and (y > 70) become
members of I, items in region (x ≤ 1500) and (y ≤ 70) become members of
R,and the remaining items fall into U . This update corresponds to utterance (8)mentioned in Section 2.

y

y

U
80

80

C

R
70

U

I
U

70

D
1500

C

R
2200

x

D
1500

U
2200

x

Fig. 3. Update of boundaries when a proposal is made from R.

Proposal from U Let us assume that < A, 2000, 90 > has been accepted
and the agent proposes < C, 2200, 80 >. As C is in U , the agent needs to get
conﬁrmation from the user. If the user answers “Good,” the boundaries change asshown in Fig. 4. Items in region (x > 2000) and (y > 90) and region (x > 2200)

and (y > 80) become members of I, and items in region (x ≤ 2000) and (y ≤ 90)
and in region (x ≤ 2200) and (y ≤ 80) become members of R. The remaining
items fall in U . This corresponds to utterances (2) and (6) mentioned in Section
2.

y

y

I

U
90

90

A

80

A

C

R

U
2000
00

U

80

C

R

I

U

2200
2
00

x

U

2000

x

2200
2 00

Fig. 4. Update of boundaries when a proposal is made from U and the response is“Good.”In the above situation, if the user answers “No good,” boundaries change
as shown in Fig. 5. As B is not accepted, boundaries for R do not change.
Boundaries for I are updated in the same way as when the user answers “Good.”This corresponds to utterance (4) mentioned in Section 2.

y

y

I

U
90

A

80

2500
2 00

x

U
B

R

U
2000
00

A

80

B

R

I

U
90

U
2000

2500
2 00

x

Fig. 5. Update of boundaries when a proposal is made from U and the response is “Nogood.”

With the rational recommendation method, an agent can propose items thatare appropriate given the course of dialogue. There may be multiple items inregions R and I, but how to choose one of them depends on the agent’s strategy.
If the agent is rewarded for securing the user’s acceptance, it may well propose
items in decreasing order of the reward amount.

Here we discuss cases where agents deal with only two attributes for simplicity
of discussion, but we can apply the above method to more than three attributes
cases.

5

Macroscopic Behavior of Recommendation Battlers

In this section, we show the competitive nature of Recommendation Battlers
aﬀects the output (an item that is ﬁnally accepted by the user) of the systemfrom a macroscopic view. It is supposed that the behavior of Recommendation
Battlers changes depending on the number of agents. When there is only a single
agent, it is clear that it behaves like an independent recommendation system andno competition occurs. As the number of agents increases, the agents become
more competitive to recommend items to the user, and that leads to an increaseof the user’s utility and an decrease of the agents’ utility. In this section, we
verify the above supposition through simulation experiments.
5.1

Simulation Settings

As settings for simulation, we have the following assumptions.
– Each item Ci has three independent attributes xi,1 , xi,2, xi,3 and each attribute takes a value from 0 to 1 at random.
– The user’s utility, when he/she accepts an item Ci , is calculated from theﬁrst and second attribute values as follows.
11(1)xi,1 + xi,2 ,22where Ci is an item whose attribute values are speciﬁed as xi,1 and xi,2 .
In a restaurant recommendation system, these attributes correspond to theaverage cost and the distance from the nearest station.
– The agent’s utility, when its recommended item is ﬁnally accepted by theuser, is identical with the third attribute value of the item.
Uu (Ci ) =Ua (Ci ) = xi,3(2)
In a restaurant recommendation system, this attribute corresponds to thereward from the an information provider when the user ﬁnally accepts therecommendation.
– Each agent recommends items following the method mentioned in Section
4. When it has more than two items to recommend, it chooses one with thehighest Ua (·).
Our simulation experiment is performed as follows.
1. We prepare items with randomly generated attributed values and equallyassign them to each agent.
2. We then run a system to get an item that is ﬁnally accepted by the user andcalculate the utility values of the user and the wining agent.
3. We perform the above experiments 1000 times and calculate the average ofthe utilities.

5.2

Results

We show the utilities of the user and the winning agent in Fig. 6 and Fig. 7
respectively when we change the number of agents and the number of items.

0.92

User's Utility

0.9

2 Agents
3 Agents

0.88

5 Agents
0.86

10 Agents
20 Agents

0.84
0.82
100

200

500

1000

2000

Number of Items
Fig. 6. User’s Utility

Fig. 6 and Fig. 7 show that the user’s utility increases but that the agents’utility decreases as the number of agents increases. This is because more agentsraise more competitions among them and this makes a beneﬁt to the user. The
user can get an item that is more preferred. The results also show that both ofagent’s and the user’s utilities increase as the number of items increases. Thisis because the probability that the ﬁnally accepted item is beneﬁcial for both of
the user and the agent increases as the number of items increases.

6

Related Work

Information recommendation system provides information to the user from thesystem side. This is contrasting with conventional information retrieval systems
in which the user needs to operate a system by forming and submitting a query. In
other words, for a user to obtain information, the user is passive in an information
recommendation system, and he/she is active in an information retrieval system.
In general, an information recommendation is eﬀective when the user does nothave clear requirement or preference nor has a prior knowledge about information
sources. Hence, information recommendation systems facilitate a function toestimate the true preference of the user and a function to interact with the0.85

Agent's Utility

0.8
2 Agents

0.75

3 Agents

0.7

5 Agents

0.65

10 Agents

0.6

20 Agents

0.55
0.5
100

200

500

1000

2000

Number of Items
Fig. 7. The Winning Agent’s Utility

user. ATA(Automated Travel Assistant)[6] and Expert Clerk[7] are examples of
information recommendation systems.
ATA is a system for assisting the user in his/her travel planning. In ATA,CCA(candidate/critique agent) learns the user’s model through conversational
interactions with the user. CCA represents the user’s model as a multi-attribute
utility function and learns it by adjusting the function and the weight.
ExpertClerk is a system that navigate a customer on the Internet by using
a commodity database and a life-like agent. In ExpertClerk, a life-like agent
talks with the customer in a natural language by using two navigation modes;
NBA(Navigation by asking) and NBP(Navigation by proposing). NBA produces
questions that gain most information on the user’s preference by using a hierarchical concept base. NBP displays three contrasting goods to the user to know
the user’s preference from another viewpoint.
Both of ATA and ExpertClerk are information recommendation systems that
try to recommend items to the user through interactions with him/her, but eachof them is a single agent system in which the number of employed agent is onlyone.
Inhabited Market Place[1] has been developed by Andre and her colleagues
at DFKI. In this system, multiple animated agents appear on the display andeach of them tells its own opinion on an item. For example in a car dealing,an agent tells about the speed of car and another agent tells about the fuelconsumption. Hearing and comparing the opinions, the user makes his/her own
decision. Agents in this system just tell their opinions following a scenario anddo not change them according to the responses from the user. Their work mainly
emphasize the advantage of multiple agents as a presentation media.

7

Future Work

Our future works are summarized as follows.
Eﬃcient recommendation. From a viewpoint of the user, he/she wants toobtain preferred items as soon as possible rather avoiding a long transactions
with the agents. We need to continue to study how to reduce the number ofrecommendations when there are a large number of items to recommend.
Shared Ontology. Recommendation Battlers provides a platform where multiple agents compete to recommend items. In the framework, an agent can use
responses from the user to items recommended by other agents. To facilitate
this mechanism in a general context, agents need to mutually understand theattributes of recommended items. To this end, we need a common ontologythat can be shared by agents.
Conversation Capability. In our current prototypes, the user interact withagents by saying only “Good” or “No good,” but this is restrictive as arelevance feedback mechanism. For example, a user may respond “No good”
because of an experience of bad taste not because of the price or the locationof restaurant, but the agent cannot understand the true meaning of “Nogood.” For more complex interactions with the agents, natural language
processing or conversation capability is required for agents.
Scalability. In this paper, we just discuss Recommendation Battlers consistingof two or three agents. We need to further study cases where many agentscompete to recommend items. At ﬁrst, it seems diﬃcult that a single userinteracts with many agents one time because of at least two reasons. First,
the user gets tired of a long transaction of recommendation involving many
agents. Second, it is diﬃcult to display many agents on a single browser on
the user’s PC at once.
A possible solution maybe employ a middle agent between the user and therecommendation agents. At ﬁrst, a user interacts with a small number of
agents and the middle agent records the interactions. When there are moreagents to recommend items, they ﬁrst interact with the middle agent, andthe middle agent checks them whether they have at least one item worth torecommend referring to the history of previous recommendations. Only whenthe middle agent permits, a recommendation agent can directly interact withthe user.

8

Conclusion

We proposed a competitive information recommendation system where multiple
animated agents recommend items in a competitive and interactive manner. We
discussed the advantages of the framework through showing a prototype applied
to restaurant recommendation. We also showed how its macroscopic behavior
was aﬀected by the number of participating agents.
Including the future work discussed in the previous section, we also continue
to work to show the applicability of this framework to real-world domains.

Acknowledgement
This work is partly supported by grants from NEDO (New Energy and Industrial
Technology Development Organization), Hitachi Cooperation, and the Ministryof Education, Culture, Sports, Science and Technology.

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