Navigating Users Based on Estimation of Interest Vectors with Utility Function

Fumiko Harada† Yosuke Nakagawa† Hisashi Ishii‡ Hiroyuki Yamahara‡

Hiromitsu Shimakawa†

† College of Information Science and Engineering, Ritsumeikan University‡ Graduate School of Science and Engineering, Ritsumeikan University

1-1-1 Noji-higashi, Kusatsu, Shiga, Japan{harada,yosuke,hisashi,yama,simakawa}@de.is.ritsumei.ac.jp

Abstract

In large-scale trade shows, there are two kinds of con-nections dependent on interests of attendees: connectionsbetween attendees and booths, and that between booths andbooths. The large-scale-ness, however, prevents the estab-lishment of these connections because each attendee cannotobtain the entire information on all of his interesting booths.

In this paper, we propose a personal navigation systemto promote establishment of these connections by providingthe information of unvisited booths which are interesting toeach attendee. The proposed system numerically estimateseach user’s interest from the history of visited booths andrecommends unvisited booths according to his estimated in-terest through a mobile device and RFID tags installed inbooths. By recommending booths with a floor map, their lo-cations, his current position, and quantitative bases of rec-ommendation, each attendee can select and visit one of rec-ommended booths according to these satisfaction levels andlocations.

1. Introduction

Recently, many large-scale trade shows are held todemonstrate the most advanced products, solutions, ser-vices, and technologies around the world (i.e. CEATECJAPAN [1], International CES [3], Macworld Conference &Expo [4], and so on). In most of trade shows, exhibitors setup booths in show floors to demonstrate their exhibits. Eachattendee visits a booth to see its content if it is interesting tohim. Such situations bring two kinds of connections depen-dent on interests of attendees: connections between atten-dees and booths, and that between booths and booths. Thefirst kind of connection is established when each attendeeknows what fields of contents are displayed in booths, rec-ognizes whether each booth’s content is interesting to him

or not, and visits the interesting ones. The second kind ofconnection implies the association among booths based onthe field of their contents. When an attendee is interestedin content of a booth, he may want to visit the other boothswhose contents involve the fileds similar to the former one.Thus, the second kind of connection is established wheneach attendee knows which booths’ contents are associatedwith that of visited booths and visit them.

In large-scale trade shows, however, establishment ofthese connections are prevented because of their large-scale-ness. Each attendee checks what booths will be setup and interesting to him in advance. He also checks whatunvisited booths are interesting to him by walking around inthe show floor or by receiving brochures and leaflets aboutbooths. These actions will leads to the establishment of thefirst kind of connections. However, because of vast showfloor and the multitude of booths, he cannot obtain the en-tire information on all interesting booths. Additionally, be-cause of the multitude of attendees, the exhibitor of eachbooth cannot deliver its brochures and leaflets to all atten-dees who will be interested in it. Thus, the connection be-tween attendees and booths may not be established com-pletely. Similarly, the complete connection between boothsand booths also may not be established. There are so manybooths whose contents are associated with each other. Anexhibitor would want attendees who have visited associatedbooths to visit his booth. An attendee may want to visitanother associated booth if a visited booth is interesting tohim. The booths, however, are set up independently eachother and associations between contents of booths are notannounced explicitly. In fact, for example, over 140,000 at-tendees and 2,700 exhibitors have participated at CES 2007held in 1.8 million net square feet of exhibit space [3]. It isalmost impossible for attendees and exhibitors to obtain theinformation on all booths and their contents. The establish-ment of the connections mentioned above gets all the moredifficult for the large-scale-ness to excite attendees and ex-

hibitors.In this paper, to solve these problems, we propose a per-

sonal navigation system to establish these connections inlarge-scale trade shows by providing the information on un-visited booths to each attendee according to his interest.Our proposed system estimates the user interest based onthe history of visited booths and the satisfaction levels ofthem, and recommends booths according to the estimatedinterest. The advantages of our proposed system are as fol-lows:

• It can make an automatic and quantitative estimationof the user interest from the history of visited boothsinstead of manual input of the interest by the attendeehimself, which does not require that attendees figureout their interest completely.

• It may be a case that the user is not satisfied with thecontent of a visited booth even if it seems to be in-teresting at a glance. Since our proposed system es-timates the user interest considering user satisfaction,its recommendation is more correct than that made bya system which considers only whether the user hasvisited each booth or not.

• The user can select one of the recommended booths atchoice with referring the location of the recommendedbooths and quantitative basis of recommendation.

This system enables attendees to visit booths which are in-teresting even if they do not know the entire information ofall booths. The two kinds of connections mentioned abovein large-scale trade shows are promoted to be established.

This system directly provides services to only attendeesthrough mobile devices while exhibitors receive benefits in-directly. Therefore, we use the word “a user” as an attendeein the following.

The rest of this paper is organized as follows. Section2 proposes a personal navigation system which estimateseach user’s interest and recommends unvisited booths ac-cording to his estimated interest. In Section 2, numericaldescription of an interest is introduced and the overview ofthe proposed system is described. The detail of how to esti-mate user’s interest and determine the recommended boothsare presented in Section 3. Section 4 shows an implemen-tation of the proposed system. Comparison of the proposedsystem with related works is discussed in Section 5. Finally,Section 6 concludes this paper.

2. Personal navigation system with utility-based booth recommendation

To establish the connections in large-scale trade shows,we propose a personal navigation system which estimates

the user interest and recommends booths. At first, numeri-cal representation of characteristic of a booth, which is uti-lized to quantify the user interest, is introduced in Subsec-tion 2.1. The system overview how the system providesbooth recommendation to users is given in Subsection 2.2.

2.1. Characteristic vector of a booth

Attendee decides to visit a booth if its content is inter-esting to him. Each booth in the show floor involves somefields of existence products, solutions, technologies, and soon. For example, the content about cellar phones with or-ganic electroluminescence display has associations with thefields “mobile devices” and “organic electroluminescencetechnologies”. The degrees of involvement differ amongfields and booths. To describe the associated fields, we in-troduce a characteristic vector of each booth which repre-sents the fields of its content and the degree of associationwith each field numerically.

Each dimension of a characteristic vector is correspond-ing to a field which can be involved by booths in the showfloor. The value of a dimension represents the degree of as-sociation to its corresponding field. If the value of a dimen-sion is zero, the content of the booth is not associated thefield at all. For example, the content of cellar phones withorganic electroluminescence display can be represented as “mobile devices”

“organic electroluminescence technologies”“white goods”

=

0.50.10.0

.

We assume a situation where the fields corresponding toeach dimension of characteristic vectors is standardized allover the show floor and that each exhibitor sets a character-istic vector of his booth in advance.

Such vector also can describe the trends of user inter-ests. If an attendee figures out his interest as a vector, boothrecommendation is easily performed by recommending thebooths whose characteristic vectors agree with the user in-terest. However, it is troublesome to figure out the interestsince an attendee may be interested in many different fieldswith different degrees. To solve this problem, automatic es-timation of the user interest may be required.

2.2. System overview

The proposed system estimates the user interest from thehistory of visited booths and recommends unvisited boothsto him according to the estimated interest. It is used in anenvironment where RFID tags are installed in all of booths

Software development

Ubiquitous computers

Characteristic vector of booth C

User

Booth C RFID Tag

History of visited characteristic vectors

Characteristic vector of booth C

Organic electroluminescence

Software development

Ubiquitous computers

Organic electroluminescence

Figure 1. Acquisition of the characteristicvector of new visited booth C

in the show floor and each attendee has his own mobile de-vice equipped with an RFID reader. The ID and character-istic vector of a booth is stored in the corresponding tag. Weassume that all of the IDs and characteristic vectors are alsostored in users’ mobile devices a priori. Such situation canbe realized in case that each mobile device obtains these in-formation from a database server equipped in the show floorfor example.

Each user visits a booth which seems to be interestingto him. After the end of visiting the booth, he reads theRFID tag installed in it through his mobile device. He givesthe mobile device the satisfaction level, which means howmuch he is satisfied with the content of the visited booth.The satisfaction level is utilized for estimation of the userinterest. The ID, characteristic vector, and satisfaction levelof the booth are stored in the mobile device as the visitedbooth history.

After he has visited booths more than a specific numberof times, the mobile device estimates his interest based onthe history of the visited booths and their satisfaction levels.The interest is estimated by identifying the distribution ofsatisfaction levels with respect to the characteristic vectorsof booths. Based on the estimated interest, a certain numberof unvisited booths are selected as recommended booths.The detail how to determine the recommended booths isgiven in Section 3.

The mobile device indicates the recommended boothswith a map of the show floor on its screen. The map pro-vides the current position of the user, locations of the rec-ommended booths, and quantitative bases for the recom-mendation, where a quantitative basis is a measure to pre-dict how he will be satisfied with the content of each rec-ommended booths. Referring to the locations of the boothsand quantitative bases, the user can select one of the recom-mended booths and visit it. For example, if he emphasizesthe degree of satisfaction more than avoiding walking in a

vast show floor, a user can visit a recommended booth withhigh satisfaction level even if its location is far from his cur-rent position. Vise versa, he also can visit a near booth if heemphasizes the location of booths more than the degree ofsatisfaction.

The booth recommendation is updated in every time hevisits a new booth by the same way, which makes the esti-mation of his interest more precise.

This system enables attendees not only to visit the inter-esting booths without knowing the information of all boothsbut also to visit booths involving the same fields of visitedbooths. Thus, it brings establishment of the two kinds ofconnections mentioned in Section 1.

3. Utility-based booth recommendation rule

This section describes how to select the recommendedbooths among unvisited booths on the mobile device fromthe history of visited booths and their satisfaction levels.

Numerical processing of the history must be easily per-formed on mobile computers even if the size of the historyis large. We propose to recommend booths according to thefollowing steps:

1. History reduction: Reducing the history of character-istic vectors into 1-dimensional data. This step aims atmaking it easy to numerically process the history on amobile device.

2. Estimation of utility function: Estimating the utilityfunction which represents the distribution of satisfac-tion levels with respect to the reduced characteristicvectors.

3. Ranking booths and recommendation: Making rec-ommendation ranking of unvisited booths based on theestimated utility function for finding the most satisfy-ing booths.

The details of these steps are given in below.For convenience of notation, we denote the number

of visited booths and dimensions of each booth’s char-acteristic vector as n and k, respectively. Let V i =[vi1, . . . , v

i2, . . . , v

ik

]be the characteristic vector of the ith

visited booth.

3.1. History reduction

In the visited booth history stored in a mobile device,the characteristic vectors of booths are represented as k-dimensional vectors. If the number of dimensions k of char-acteristic vectors is large, it will be numerically complicatedto treat them on mobile devices. Reduction of data in the

Software development

Ubiquitous computer

Organic electroluminescence

Principal component analysis

Software development

Ubiquitous computer

Organic electroluminescence

The first principal component axis

The centroid of the characteristic vectors

Figure 2. Calculating the principal compo-nent from the history

history is significant in order to deal with the booth charac-teristic vectors and process the history. We apply the princi-pal component analysis [8], called PCA, to the set of char-acteristic vectors in the visited booth history. The principalcomponent analysis is a useful tool to represent multivari-ate data as single-variable data with few loss of informationfrom original one. Each characteristic vector V i is reducedinto a scalar si (called principal component score).

Shown in Fig. 2 is an illustration of PCA. The up-per graph of Fig. 2 shows an example where the charac-teristic vectors of visited booths are represented as three-dimentional vectors involving “software development”, “or-ganic electroluminescence”, and “ubiquitous computing”.According to the PCA, reduction of these vectors intoscalars is performed by finding the first principal compo-nent axis. It is the projection of the characteristic vector seton the direction in which the variance of the projection ismaximized. In Fig. 2, the projection from each characteris-tic vector into the broken line maximizes the variance of thereduced data, which implies that the projection is the bestsummary of the characteristic vectors with the least loss ofinformation.

We review the reduction of characteristic vector V i intoscore si through the PCA [8]. The correlation matrix C of

Utility function

XY ZXY ZUnvisited

booths

・・・・・・ ・・・・・・ P5

Y2

X1 ・・・・・・ ・・・・・・ P5

Y2

X1

Recommendation ranking

Principal component

score

utility

Utility

Principalcomponent

score

+

Figure 3. Interest estimation and unvisitedbooth recommendation

the visited characteristic vectors {V1, . . . , Vn} is given as

C :=

C11 C12 · · · C1k

C21 C22 · · · C2k

......

. . ....

Ck1 Ck2 · · · Ckk

, (1)

Cij =n∑

p=1

(vpi − vi)(v

pj − vj)

ni, j = 1, 2, . . . , k, (2)

where

vi =n∑

p=1

vpi

n, i = 1, . . . , k. (3)

Matrix C has n real number eigenvalues since it is symmet-ric. The first principal component axis is mathematicallydefined as the eigenvector w =

[w1, . . . , wk

]correspond-

ing to the maximum eigenvalue of C. The projection fromV i to the first principal component axis is calculated ac-cording to the following equation:

si =k∑

p=1

wp

(vi

p − vp

). (4)

3.2. Estimation of utility function

Though we assume that characteristic vectors of boothsis defined by their exhibitors, it can bring the case that theimage of a field involved by the content in an exhibitor dif-fers from that in a user. This fact implies that the user isnot satisfied with the booth even if its content seems to beinteresting at a glance and the fields which the user is in-terested in agree with that involved by the booth. For ex-ample, an exhibitor sets a characteristic vector of his boothwhich significantly involves in the “software development”dimension. A user who visits this booth is also interested in“software development”. Simply considering, the user maybe satisfied with the booth since its involving field agreeswith his interest. In actual, it is possible that the exhibit

of the booth is newly developed programming languageswhile the user is interested in integrated development envi-ronments. This case makes him unsatisfied with the booth.This is brought from the gap between the images of “soft-ware development” in the exhibitor and the user.

This fact implies that characteristic vectors and their re-duced data of a history cannot be directly utilized for de-scription of the user interest. To describe user interest moreprecisely, we introduce utility function, which digitalizeshow much the user will be satisfied with each booth.

The utility function is calculated from the principal com-ponent score si and satisfaction level ui of each visitedbooth V i. A mathematical model of the utility function isgiven a priori, which is denoted by U(·; α, β, γ, . . . , ) witha set of finite number of parameters α, β, γ, . . .. Identifi-cation of parameters α, β, γ, . . . is performed by applyingthe least squares method to the data {si, ui}n

i=1. Namely,the problem is formulated as follows: Find the parametersα = α∗, β = β∗, γ = γ∗, and so on which minimize

n∑i=1

(ui − U(si; α, β, γ, . . .))2. (5)

For example, suppose that the mathematical model of autility function is given by the following equation with threeparameters α, β, and γ:

U(s;α, β, γ) = αs2 + βs + γ. (6)

In this case, the parameters α = α∗, β = β∗, and γ =γ∗ which minimize Eq.(5) are calculated. Identification ofparameters enables prediction of satisfaction level for anyunvisited booths.

3.3. Ranking booths and recommendation

For an unvisited booth with its characteristic vector e =[e1, . . . , ek

], the estimated satisfaction level of it is given

by

U

(k∑

p=1

wp(ep − vp);α∗, β∗, γ∗, . . .

), (7)

where α∗, β∗, γ∗, and so on are the identified parametersof the utility function by the way mentioned in the previoussubsection. A booth recommendation ranking of unvisitedbooths is made according to the estimated satisfaction levelsof them. A certain number of the unvisited booths with thehighest estimation satisfaction levels are selected from theranking for recommendation.

Figure 3 shows an illustration of determination of rec-ommended booths. Utility function with principal compo-nent score of each visited booth is estimated. After the util-ity function is estimated, the principal component score and

satisfaction level of the unvisited booths X, Y, Z, . . . are cal-culated. The highest P booths are selected from the rank-ing. These booths are shown on the screen of the mobiledevice with the map and quantitative basis.

As the quantitative basis of the booth recommendation,the similarity between each recommended booth and vis-ited booths is utilized. For example, the following equationimplies the sum of the average cosine similarity:

1n

n∑i=1

vi · e∥vi∥ · ∥e∥

. (8)

As the value of Eq. (8) is closer to 1, the involving fieldsand its degree of a recommended booth e are similar to thatof visited booths.

Quantitative basis (8) is displayed with the location ofthe corresponding booth on maps displayed on screen ofmobile devices. Referring both of the value of Eq. (8) andlocations of recommended booths brings each user a flexi-ble selection of booths to visit. Furthermore, recommenda-tion of multiple booths gives him more information aboutbooths than that given by single booth recommendation.

4. Implementation

We made a partial implementation of the proposed sys-tem on HP IPAQ hx2490b [2]. The proposed personal nav-igation application is developed in C# on the operating sys-tem Microsoft Windows Mobile 5.0 [5].

We suppose that the characteristic vectors are ten-dimensional．For fitting of a utility function, we apply asecondary-order function, namely Eq. (6). Digitalization ofthe satisfaction level is evaluated by whether the user ob-tained some new knowledge by visiting each booth.

When a user reads an RFID tag, the IPAQ mobile devicedisplays an entry screen of the satisfaction level of the vis-ited booth as shown in Fig. 4. It is questionnaire includingfive questions. The user marks to the check box next to eachquestion if he agrees with it. The number of marked checkbox is evaluated as the satisfaction level from zero to five.

After reading the RFID tags of booths more than fivetimes, the booth recommendation ranking is shown on thedisplay as shown in Fig. 5. The recommendation rankingprovides the recommended booths in decreasing order withtheir IDs and estimated satisfaction levels.

This implementation excludes displaying maps witha user’s current position and locations of recommendedbooths. Realization of displaying of current position re-quires monitoring the user’s current position through a po-sitioning technology. Applying a positioning technology toIPAQ is one of our future works. Additionally, the entireinformation of the show floor (IDs and characteristic vec-tors of all booths and map of the floor) must be stored in

Figure 4. Display when booth evaluation

Figure 5. Display of booth recommendationranking

the IPAQ in advance. To obtain such information, a com-munication protocol and technology may be also requiredfor reception of information from a server equipped in theshow floor.

5. Related works

Our proposed method is one of personal navigation sys-tems using maps based on user interests for establishingconnections between users and objects. Some similar per-sonal navigation systems have been proposed for city toursor exhibitions [6, 7, 10, 12]. Cyberguide [6] is a personalnavigation system based on maps for a city tour. Thoughit does not explicitly utilize user interest for the navigation,

the authors have suggested an extension with consideringthe user interest. In GUIDE [7], personal navigation basedon user interests is performed after users configure his in-terests explicitly a priori. This is troublesome more thanour proposed system estimation of user interest in our sys-tem. UbiNEXT is a personal navigation system in exhibi-tion which is most similar to our system. UbiNEXT se-lects recommended exhibits according to keywords associ-ated with exhibits already seen. The keywords are providedto users as the recommendation bases. However, providedkeywords may not always represent the user interest since itmay be a case that an user is not familiar with the providedkeywords. This situation may discourage users’ curiosityto the recommended exhibits. On the other hand, our pro-posed system quantitatively estimates the user interest andprovides quantitative basis. Such recommendation may notdiscourage users’ curiosity.

Our proposed system performs a booth recommendationbased on characteristic vectors of visited booths. Similarmethod have been applied to information recommendationor filtering through history of visited web pages [11, 13].In these studies, the vector space model [14] has been intro-duced to characterize visited documents, where each dimen-sion of vector corresponds to a keyword and its value rep-resents the frequency of appearance of corresponding key-word in the document. While documents have an explicitmeasure for the vector space model (frequency of appear-ance of keywords for example), booths do not have explicitand numerical measure for exhibitors to define the charac-teristic vectors. It will be required to find some indicatorsfor definition of characteristic vectors of booths.

One of the famous techniques for recommendation is thecollaborative filtering (CF) [15, 16] applied in Amazon.com[9]. The main concept of the CF is to find objects with sim-ilar evaluation values from users. From the point of viewof the connection between booths and booths, CF can makemore accurate recommendation than our proposed methodsince it explicitly considers the similarity among visitedbooths. However, CF requires larger variety of data andconsumes more computation time. Since an attendee can-not visit so many booths, CF may not be suitable for thebooth recommendation with small number of visited boothson mobile devices.

6. Conclusion

In large-scale trade shows, there are two kinds of con-nections dependent on interests of attendees: connectionsbetween attendees and booths, and that between booths andbooths. The large-scale-ness, however, prevents the estab-lishment of the connections because of each attendee cannotobtain the entire information on all of booths. In this paper,we have proposed a personal navigation system to establish

the connections by recommending unvisited booths to eachuser according to his interest through RFID tags installed inevery booth and a mobile device. We also have shown a par-tial implementation developed on an IPAQ mobile device.

Since the proposed system estimates the user interestquantitatively from the history of visited booths and theirsatisfaction level, the user need not to figure out his interestexplicitly a priori, this is easy to use. Furthermore, recom-mended booths are displayed on the screen of mobile deviceof each user with the map of show floor, his current position,and a quantitative basis of recommendation. Thus, he cancompare the recommended booths and select one booth tovisit at choice from two aspects of evaluation: the locationand estimated satisfaction level of booths.

Future work aims to complete implementation of the pro-posed system completely. For example, we will extend theimplementation shown in Section 4 to show the map, theuser’s current position, and a quantitative basis when boothrecommendation. It is also future work to confirm the va-lidity of our proposed method to estimate the user interestand its efficiency through an experiment.

References

[1] CEATEC JAPAN. http://www.ceatec.com/2007/en/visitor/.

[2] HP united states - computers, laptops, servers, printers, &more. http://www.hp.com/.

[3] International CES. http://cesweb.org/default.asp.

[4] MacWorld Conference & Expo. http://www.macworldexpo.com/live/20/.

[5] Microsoft Windows Mobile - Smartphone and Pocket PCSoftware with ActiveSync. http://www.microsoft.com/windowsmobile/default.mspx.

[6] G. D. abowd, C. G. Atkeson, J. Hong, S. Long, R. Kooper,and M. Pinkerton. Cyberguide: A mobile context-aware tourguide. ACM Wireless Networks, 3(5):421–433, Oct. 1997.

[7] K. Cheverst, K. Mitchell, and N. Davies. The role of adap-tive hypermedia in a context-aware tourist guide. Communi-cations of ACM, 45(5):47–51, May 2002.

[8] I. Jolliffe. Princopal Componenet Analysis. Springer, 2ndedition, 2002.

[9] G. Linden, B. Smith, and J. York. Amazon.com recommen-dations: Item-to-item collaborative filtering. IEEE InternetComputing, 07(1):76–80, 2003.

[10] A. Masuoka, T. Fukawa, T. Takahashi, M. Takahashi, andS. Ito. ubiNEXT: A new approach to support visitor’s learn-ing experience in museums. In Parallel Paper Presentationsof the 11h International Conference on Human-ComputerInteraction, July 2005.

[11] R. Meteren and M. Someren. Using content-based filter-ing for recommendation. In Proceedings of CML/MLNETWorkshop on Machine Learning and the New InformationAge, pages 47–56, May 2000.

[12] R. Oppermann and M. Specht. A context-sensitive nomadicexhibition guide. In Proceedings of the 2nd InternationalSymposium on Handheld and Ubiquitous Computing, pages127–142, Sept. 2000.

[13] M. J. Pazzani and D. Billsus. Learning and revising userprofiles: The identification of interesting web sites. MachineLearning, 27(3):313–331, June 1997.

[14] G. Salton and M. J. McGill. Introduction to Modern Infor-mation Retrieval. McGraw-Hill, 1986.

[15] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-basedcollaborative filtering recommendation algorithms. In WorldWide Web, pages 285–295, 2001.

[16] J. Wang, A. P. de Vries, and M. J. T. Reinders. Unifyinguser-based and item-based collaborative filtering approachesby similarity fusion. In Proceedings of the 29th annual in-ternational ACM SIGIR conference on Research and devel-opment in information retrieval, pages 501–508, 2006.