Rules for Usng Mult-Attrbute Utlty Theory for Estmatng a User s Interests Ralph Schäfer 1 DFKI GmbH, Stuhlsatzenhausweg 3, 66123 Saarbrücken Ralph.Schaefer@dfk.de Abstract. In ths paper, we show that Mult-Attrbute Utlty Theory (MAUT), a prescrpton for evaluatng objects, can be ascrbed as evaluaton process to a user when estmatng the user s nterests. Some rules are proposed for the applcaton of MAUT. Keywords: User s nterests, MAUT, evaluaton scheme 1 Usng MAUT for Estmatng Varous Interests of a User Estmaton of a user s nterests n recommender systems s very mportant, because these nterests are the bass of the system s recommendatons. There are many approaches for estmatng the user s nterest n the user modellng area. One approach s to ascrbe the user Mult-Attrbute Utlty Theory, abbrevated as MAUT, (see [WE86]) as evaluaton process. There are many approaches for estmatng the user s nterest and a great number of them do not explctly menton usng MAUT. Wthn ths paper, we wll have a detaled look at some of these approaches and examne the followng ssues: Can MAUT be used as common denomnator of these approaches (although MAUT s not explctly mentoned n the descrptons of these approaches)? Is t possble to defne rules descrbng how to apply MAUT for estmatng the user s nterests? For answerng these questons, we sketch out MAUT n Secton 2. The followng Secton 3 summarses a selecton of recent approaches whch estmate the user s nterests and Secton 4 proposes some rules for usng MAUT. 2 Mult-Attrbute Utlty Theory (MAUT) Mult-Attrbute Utlty Theory s an evaluaton scheme whch s very popular by consumer organsatons for evaluatng products. For example, n Germany Stftung Warentest uses MAUT (see for example [St00]). 2 Accordng to MAUT, the overall evaluaton v(x) of an object x s defned as a weghted addton 3 of ts evaluaton wth respect to ts relevant value dmensons [WE86]. The common denomnator of all these dmensons s the utlty for the evaluator. For example, a dgtal camera can be evaluated on the value dmensons qualty of mage, flash, 1 Ths work has been supported by the EC through ts IST-Programme under contract IST-1999-10688 CAWICOMS (see http://www.cawcoms.org for addtonal nformaton). 2 For supportng a person n evaluatng an object by usng MAUT, one could use sophstcated vsualzaton tools, but these thngs are out of scope of ths artcle. 3 There are other possbltes for aggregaton whch are descrbed by [WE86].
vewfnder, operaton tme, and handlng (see [St00]). The overall evaluaton s defned by the followng overall value functon: v( x) = n = 1 w v ( x) Here, v (x) s the evaluaton of the object on the -th value dmenson d and w the weght determnng the mpact of the -th value dmenson on the overall evaluaton (also called the relatve mportance of a dmenson 4 ), n s the number of dfferent value n = 1 dmensons, and w = 1. For each value dmenson relevant attrbutes: 5 d the evaluaton v (x) s defned as the evaluaton of the v ( x) = w v ( l( a)) a A Here, A s the set of all attrbutes relevant for d, v a ( l( a)) s the evaluaton of the actual level l(a) of attrbute a on d. w a s the weght determnng the mpact of the evaluaton of attrbute a on value dmenson d. w a s also called relatve mportance of attrbute a for d. For all d (=1,...,n) holds w a = 1. a a a A For example, for the qualty of mages, we have to consder attrbutes such as sharpness, colour reproducton, and resoluton, whereas for operaton tme, we only have a sngle attrbute, actual operaton tme, whch reflects how long the camera can be operated. In order to evaluate attrbutes, t s necessary to construct a scale representng the propertes of the levels of an attrbute. A scale from 0 (worst) to 10 (best) serves as measure of the evaluaton. 6 Very often there s already a natural scale for the levels of the attrbutes. For example, for the resoluton, we can defne that 4 megapxel or more s best (10 ponts) whereas 0 megapxel s worst (0 ponts) (see Fgure 1). 10 VALUE 0 1 2 3 4 RESOLUTION IN MEGAPIXEL Fgure 1: Example of a value functon Dependng on the attrbute, the evaluaton functon looks dfferent. If t s a contnuous varable, the evaluaton functon also wll be a contnues one, f t s a dscrete varable, the functon wll be dscrete. There s also the possblty to use value functons of 4 The relatve mportance of a dmenson also expresses the relevance of a dmenson for the overall evaluaton. 5 There could be also subdmensons nvolved. In ths case, the evaluaton of the object on a dmenson would be defned smlar to the overall evaluaton, by a weghted addton of the evaluaton of the object wth respect to ts subdmensons. 6 The boundares of ths scale can be selected to another values, e.g. [0..1], f one prefers another scale.
dfferent shape, for example logarthmc ones, tr-partte ntervals (not necessarly of equal length) or even user dependent scales. However, usually t s a good dea to use the smplest model possble for the doman for reasons of transparency (cf. Secton 4.1.2). Table 1 shows an example of the evaluatons of the attrbutes of two dfferent cameras whch are relevant for the dmenson qualty of mages. Accordng to Table 1, camera CA s evaluated on qualty of mages as follows: 10 * 0,4 + 5 *0,3 +5 * 0,3 = 0,7 For camera CB, the evaluaton s done accordngly. The evaluatons of the cameras on ther relatve value dmensons are summarsed n Table 2. Attrbute Evaluaton of attrbute of camera CA Evaluaton of attrbute of camera CB Sharpness 10 10 0,4 Colour reproducton 5 10 0,3 Resoluton 5 10 0,3 Relatve weght on dmenson qualty of mages Table 1: Evaluaton of attrbutes and relatve weghts for dmenson qualty of mages Evaluaton of Products Dmenson CA CB Qualty of mage 7 10 Flash 10 1 Vewfnder 5 9 Operaton tme 10 2 Handlng 9 3 Table 2: Examples for evaluatons of dgtal cameras on ther evaluaton dmensons Dmenson Preferences (LP) Preferences (SP) Qualty of mage 0,4 0,8 Flash 0,04 0,00 Vewfnder 0,1 0,1 Operaton tme 0,4 0,05 Handlng 0,06 0,05 Table 3: Examples for user preferences In Table 3 the preferences of two dfferent persons are depcted. A studo photographer (SP) wants to make excellent pctures. There s no need for a long operaton tme, because rechargng of batteres can be done easly and he never needs a flash. A landscape photographer (LP) who goes on long-term journeys (for example hkng n deserted areas) manly wants to have a camera whch has a long operaton tme snce s/he wll not be able to transport very many batteres. So, LP wll prefer CA whereas SP wll prefer CB (see Table 4). LP SP CA 8,24 7,05 CB 5,92 9,15 Table 4: Overall evaluatons of cameras CA and CB for persons LP and SP
Stftung Warentest uses MAUT over many years n order to evaluate products and presentng them to the publc. Even newspapers prnt the results n a shortened form, by publshng the value dmensons, the evaluaton of the products wth respect to these dmensons and the overall evaluaton of the products. Ths ndcates that an evaluaton accordng to MAUT can be easly understood. However, t should be noted that t s not trval to dentfy the value dmensons of an object and of all attrbutes beng relevant for the evaluaton. Of course, t s much easer to understand them, once they have been defned. Ths s probably the reason why people use more smpler evaluaton schemes when evaluatng objects or just rely on the schemes of consumer organsatons whch they do understand. In the followng, we wll examne some approaches for estmatng the user s nterest and examne whether the used evaluaton scheme wll be compatble wth MAUT. 3 Approaches for Estmatng the User s Interest [EMM+01] present a system for news classfcaton n the World Wde Web. The task of the system s to flter news for a sngle user. As seen from the vew pont of MAUT, the system has to determne the utlty of a gven artcle,.e. whether t s nterestng to the user or not. In the begnnng, the user selects some categores from an ontology and assgns a relevance value to them. A news tem s rated accordng to ts relevance wth respect to categores, keywords, and resource channels. From a smplfed vewpont, the user has defned nterest dmensons by specfyng the categores whch are of nterest to hm. An artcle s useful, f t s nterestng whch means that t falls n one of the selected categores. The value functons are equvalent wth the functon descrbng whether an artcle belongs to such a category or not. However, n ths case t s not as smple as descrbed n ths text, because the offered categores do not match perfectly wth the user s nterest dmensons: usually, they are too general. For ths reason, keywords and resource channels are also used by the system of [EMM+01] for flterng the news artcles. The weghts whch also determne how relevant,.e. nterestng, a news tem s for the user, depend on (among other thngs) the relevance values assgned by the user. [KOY01] presents a system whch s able to learn the user s preferences for schedulng meetngs. The nterests to be learned are varables such as duraton, day-of-week, locaton, and start-tme (regardng specfc types of meetng). The system learns the user s preferences through routne use whch enables t to gve customsed schedulng advce. [KOY01] even assumes that the nterests of the user slghtly change over tme. From the vewpont of MAUT, we have, n ths case, a flat herarchy. There are only attrbutes, but no ntermedate value dmensons. For the attrbutes the value functons are dfferent for each user. For example, the locatons of the meetngs regardng a specfc type of meetng dffer from user to user. Koychev s system therefore tres to learn the value functons of the user. The weghts of the evaluaton functon are not consdered, because the system tres to predct the perfect combnaton [LHL97] use a canddate crtque model for nteractve problem solvng. In ths approach, an automated problem solver presents canddate solutons to the user who gves feedback on these solutons. Based on ths feedback, better solutons are searched and presented to the user.
The presented system s desgned as constrant-based system and the use of MAUT s not obvous. However, for dentfyng the best soluton n the user model preferences such as prefers fewer stops are represented whch can be seen as value functons. By usng the canddate crtque model, the system s not oblged to represent the user s evaluaton process, because t s the user who evaluates the objects. However, mplctly the system s learnng the user s value functon, e.g. whether the user prefers more or less stops or whch are the user s most preferred arlnes. In the same way, the weghts are learned: f there s no soluton whch matches to all the user s needs, the user has to decde whether to prefer a soluton whch fulfls hs requrements regardng attrbute A, but not B or the other way round. In the frst case, the user expresses a greater weght for attrbute A than B. [CP01] explctly uses MAUT for travel plannng. So, t s clear that the approach s compatble wth MAUT. Stereotypcal nformaton s used for not havng to ask the user about all detals. 4 Rules for Choosng the Rght Complexty of MAUT We have seen that all examned approaches n Secton 3 ascrbe an evaluaton process to the user whch s compatble wth MAUT. It seems therefore not to be an ssue whether to use MAUT or not, but to decde whch complexty of MAUT to apply. There are many degrees at whch complexty can be vared. In the followng, rules are proposed for selectng a proper degree of complexty. 4.1 Rules 4.1.1 Herarchy of dmensons and attrbutes Descrpton: In the smplest case, there are only attrbutes (cf. Secton 2) and no hgher level dmensons (such as the d n Secton 2) whch have been assgned weghts. More complex models nvolve a herarchy of dmensons and subdmensons. Rule: There are two factors whch determne the herarchy: The applcaton doman: If there are very few attrbutes or f the attrbutes cannot be subsumed n a herarchy, then a non-herarchcal model has to be chosen. Use of the model wthn the applcaton: If your doman allows to ntroduce a herarchy to model the objects to be evaluated, t depends on the elctaton and presentaton goals of your applcaton what to use. If t s suffcent, to ask the user about the attrbutes and present them (n contrast to askng / presentng) hgher level concepts, then a flat herarchy may be chosen whch just conssts of attrbutes but not of dmensons. 4.1.2 Assumpton of gven weghts and value functons Descrpton: Snce t s possble that every user has ts own value functons and weghts, on the one hand, one approach s to try to estmate all these parameters for each user. On the other hand, one could assume some of these parameters as fxed and only estmate a part of them. Rule: If there s no doubt about the parameter s value, there s no use n tryng to estmate them. However, n most cases t s not that smple, but there s a hgh
number of a value functons whch are agreed regardng ther basc meanng. For example, nearly everybody agrees that a low prce s better than a hgh prce. In a smplfcaton, the weghts of attrbutes n the lower levels of a herarchy also can be assumed as fxed. Whch parameters of these should be assumed as fx depends on how much doubt about the actual value of ths parameters (.e. attrbute weght and values functons) you have and whether t s possble to elct the actual values of these parameters. There may be a trade-off between these crtera. So, t can only be decded for each applcaton separately. For example, even f there s some doubt about value functons and attrbute weghts, t may be reasonable to choose them as fx, because the doman s so complex that t s not feasble to ask the user about all of them. 4.1.3 Complexty of aggregaton functon Descrpton: Besdes the weghted addton as aggregaton functon whch were ntroduced here, there are more complex aggregaton functons. It s possble to derve such functon on ts own, for example for ntroducng non-compensatory factors. Rule: Keep t smple! The complexty of the aggregaton functon depends on the applcaton doman, but t should be chosen as the smplest possble functon. 4.2 Applcaton example Wthn the IST-project CAWICOMS 7 (see [CC00]) a confguraton workbench s beng developed for confgurng complex products. The nterface of ths workbench has the task to support the user n choosng parameters whch match the user s needs. Example domans are the confguraton of complex telecommuncaton swtches and IP-VPNs. In order to help the user, the system proposes default parameter values and even sets parameter values for the user. For ths purpose the user s ascrbed MAUT as evaluaton process (see [SM01]) The followng desgn decsons have been taken: Herarchy of dmensons: snce the products to be confgured are qute complex, the user cannot defne every detal. For ths purpose, the evaluaton scheme conssts not only of attrbutes but also of dmensons. In ths way, the user can be asked about hs/her nterests regardng these dmensons. Based on ths nformaton, parameter values can be selected. Assumpton of gven weghts and value functons: due to the hgh number of attrbutes, the user cannot be quered about each value functon and each attrbute weght. In addton, there s usually agreement on value functons and weghts. So, they are assumed as fxed. Complexty of aggregaton functon: the use of a smple aggregaton functon (whch uses the above descrbed weghted addton) posed no problems. So, there was no need to use a more complex aggregaton functon. 7 CAWICOMS s the acronym of Customer-Adaptve Web Interface for the Confguraton of Products and Servces wth Multple Supplers
5 Concluson and Future Work We have examned some approaches for modelng the user's nterests and therefore the user's evaluaton functon. It was shown that all these approaches are compatble wth MAUT and dffer n ther complexty. Some rules have been proposed ndcatng when to use whch complexty. In ths paper, only some work regardng the work of estmatng the user s nterests has been taken nto account. It would be nterestng to extend ths survey ncludng all relevant user modelng approaches for estmatng the user s nterests. 6 References [CC00] CAWICOMS Consortum (2000). Delverable D01 Requrements, Applcaton Scenaros, Overall archtecture, and Test Specfcaton. [CP01] Chn, D., and Porage, A. (2001). Acqurng User Preferences for Product Customzaton. In Proceedngs of the 8 th Internatonal Conference, UM 2001. [CP01] Chn, D., and Porage, A. (2001). Acqurng User Preferences for Product Customzaton. In Proceedngs of the 8 th Internatonal Conference, UM 2001. [EMM+01] Elert, S., Mentrup, A., Mueller, M. E., Rolf, R., Rollnger, C.-R., Severtsen F., and Trenkamp, F. (2001). Bkn - user adaptve news classfcaton n the world web web. In Schäfer, R., Müller, M. E., and Macskassy, S. A., edtors, Proceedngs of the UM2001 Workshop on Machne Learnng for User Modelng, pages 37-47. [Koy01] Koychev, I. (2001). Learnng about user n the presence of hdden context. In Schäfer, R., Müller, M. E., and Macskassy, S. A., edtors, Proceedngs of the UM2001 Workshop on Machne Learnng for User Modelng, pages 49-58. [LHL97] Lnden, G., Hanks, S., and Lesh, N. (1997). Interactve Assessment of User Preference Models: The Automated Travel Asssstant. In Proceedngs of the 6 th Internatonal Conference, UM97 [St00] Stftung Warentest (2000). Dgtalkameras: Pxeljagd. In: test 6/2000. Avalable va http://www.warentest.de/ [SM01] Schütz, W. and Meyer, M. (2001). Defnton ener Parameter-Herarche zur Adapterung der Benutzer-Interakton n E-Commerce-Systemen. In Proceedngs of ABIS 2001. [WE86] Wnterfeld, D. von and Edwards, W. (1986). Decson Analyss and Behavoral Research. Cambrdge, England: Cambrdge UnverstyPress.