Implementations of Web-based Recommender Systems Using Hybrid Methods

Transcription

1 Internatonal Journal of Computer Scence & Applcatons Vol. 3 Issue 3, pp Technomathematcs Research Foundaton Implementatons of Web-based Recommender Systems Usng Hybrd Methods Janusz Sobeck Insttute of Appled Informatcs Wroclaw Unversty of Techmology, Wroclaw, ul. Wyb. Wyspanskego 27, Poland e-mal: sobeck@pwr.wroc.pl Abstract Applcaton of hybrd recommendaton enables overcomng dsadvantages of the basc recommendaton methods: demographc, collaboratve and content -based. In ths paper the two consensus-based hybrd recommendaton methods are presented. Then some examples of ther mplementatons to dfferent web-based systems are shown. 1 Introducton Wth growng popularty of the web-based systems that are appled n many dfferent areas, they tend to delver customzed nformaton for ther users by means of utlzaton of recommendaton methods. We can dstngush three basc recommendaton approaches: demographc, content-based and collaboratve. The demographc approach uses stereotype reasonng, whch s manly classfcaton problem, n ts recommendatons [6] and s based on the nformaton stored n the user profle that contans manly dfferent demographc features to generate ntal predctons about the user [7]. Content-based recommendaton takes descrptons of the content of the prevously evaluated tems to learn the relatonshp between a sngle user and the descrpton of the new tems [7]. In ths method a user s supposed to lke a new tem f the tem s smlar to other tems that are lked by the user [1]. The collaboratve recommendatons are able to delver recommendatons based on the relevance feedback from other smlar users. Its man advantages over the content-based archtecture are the followng [7]: the communty of users can delver subectve data about tems together wth ther ratngs and t s able to offer completely new tems to the partcular user. All the above mentoned methods have some dsadvantages. The demographc recommendatons have two basc dsadvantages [7]: they may be too general and they do not provde any adaptaton to user nterests changng over tme. Content-based method tends to overspecalze recommendatons and t s based only on the partcular user relevance. Collaboratve recommended agents have also some dsadvantages: they offer poor predcton when the number of smlar users s small and there s a lack of the transparency n the predctons. The dsadvantages of all the above mentoned recommendaton methods may be 52

2 overcome by applcaton of the hybrd soluton. For example, the lack of adaptaton n the stereotype may be easly overcome by applcaton of collaboratve or content based recommendaton. On the other hand, dsadvantage s of the collaboratve approach of the nsuffcent number of the smlar users at the early stages of the system operaton may be overcome by applcaton of the demographc stereotype reasonng [14]. Fnally some dsadvantages of content-based recommendaton, such as overspecalzaton, may be overcome by applcaton of collaboratve approach [14]. In the followng secton the user model n the hybrd recommender system s defned. In the secton three two hybrd recommendaton methods are presented. The secton four contans descrpton of dfferent mplementatons of these two hybrd methods appled for dfferent web-based systems and fnally, n the summary the effcency of the hybrd approach and future work s dscussed. 2 User model n the web-based hybrd recommendaton A user model contans knowledge about the ndvdual preferences whch determne hs or her behavor wthn the system [11]. The man problems wth the user modelng encompass the user model representaton and acquston. Here the followng user model elements wll be presented: user profle representaton, user profle ntalzaton, dstance and smlarty functons among user profles and user profle clusterng methods. 2.1 User profle representaton User profle should contan all necessary data to model the user n recommender system. The user profle n the recommender systems could be represented by many dfferent forms: bnary vectors, feature vectors, trees, decson trees, semantc networks, Bayesan networks, etc. The feat ure vector s the most popular among many recommender systems [7]. In ths paper, the user profle s represented by a tuple that have smlar descrptve strength as the feature vector but t s used n many prevous works on nformaton systems and consensus methods that s appled for the recommendaton n the systems presented n ths paper. The tuple s a functon p: A V, where A s a set of attrbutes and V s a set of ther elementary values and ( a A)(p(a) V a ). The user profle usually contans two types of data: user data and usage data [6]. The user data may contan dfferent user characterstcs: demographc data (name, gender, address, etc.), user knowledge, user sklls and user nterests. The usage data are observed drectly from the user s nteracton wth web-based system. The usage data concernng web-based system user nterface recommendaton may contan nformaton on the nterface layout, the nformaton content and ts structure. The usage data may also contan usablty measure value of the partcular nterface settngs [4]. However the qualty of the system nterface may be dffcult to measure the doman of the HCI has worked out several methods to evaluate the user nterface for example [8]: heurstcs, questonnares and user tests. 2.2 User profles ntalzaton The ntal profle may be empty, especally n case of content -based recommendatons. In other approaches the ntal profle s qute often created from the questonnare that s flled n by the user. The questonnare usually contans nformaton on user data that contans dfferent nformaton,.e. demographc data contanng: record data (name, address, phone number, e-mal), geographc data (cty, regon, country and zp-code), user s characterstcs (sex, educaton, occupaton), and some other customer qualfyng 53

3 data. The user data may also contan nformaton on users knowledge, ther sklls, nterests and preferences and also ther plans and goals. The usage data, the second element of the user model, s observed and recorded durng the whole process of user s nteractons wth web-based systems. It may concern selectve operatons that express users nterests, unfamlarty or preferences, temporal vewng behavor, as well as ratngs concernng the relevance of these elements that are expressed by dfferent events, such as: openng a page, purchasng a product, sendng feedback nformaton to the system are stored. There are of course more sophstcated and general methods for gatherng such data, as for example DoubleClck mechansms. DoubleClck enables trackng the user web actvtes and servng personalzed advertsements [17] by usng cookes entres wth unque dentfers and placng webbug mage on every page that s to be tracked. The ntal profle may be also modfed accordng to the whole user populaton behavor, whch s the case of collaboratve recommendaton. However, ths brngs some problems wth fndng smlar users that could be solved by clusterng methods [5]. In all mplementatons presented n ths paper, the user profle s created from the questonnare that s flled n by each user durng the regstraton process that s oblgatory for each user. Ths s necessary because we use demographc recommendaton. The user profle s also modfed durng workng wth the system by each partcular user. These data s then used n the collaboratve and content based recommendaton. 2.3 Dstance and smlarty between user profles The dstance functon between values of each attrbute of the user profles s defned as a functon δ at : V a V a [0,1] for all a A. Ths functon should be gven by the system nterface desgner and fulfll all the dstance functon condtons, but not especally all the metrcs condtons, and should be determned for each attrbute and ts every par of atom values. The dstance functon values could be enumerated or gven n any procedural form. The dstance between user profles could be defned n many dfferent ways. Frst, the dstance between tuples and could be defned as a sum of dstances between values of each attrbute: at d(r,r ) = d (r(a),r(a)). a A Second, the root of the sum of squares of these dstances, or fnally thrd, we also can ndcate the mportance of each attrbute a by multplyng the dstance by approprate factor defned as a functon c: A [0,1]: at d ( r,r ) = [ c( a) d ( r ( a),r ( a))]. a A However, t s also possble to use some smlarty functons nstead of dstance functons. Very popular among them s the cosne smlarty functon [13]: m m k,, = 1 ( u, uk ) =, s ( u u ) u m 2, = 1 = 1 ( u ) 2 k, where u and u k are vectors of real values of m attrbutes descrbng users and k respectvely. 54

4 We can fnd qute many dfferent smlarty functons for example n the work [13], the functon used n the Dattola clusterng algorthm [2] s shown below: m s ( u, u ) = mn( u, u ). k = 1 In all mplementatons presented n ths paper, we appled the thrd dstance, the one wth a factor that defnes mportance of each attrbute. Ths dstance s rather easy to mplement, however qute expressve. For fndng the smlarty between the user profle and tems descrpton we used the cosne smlarty functon that s the most popular one especally n the area of Informaton Retreval (IR). 2.4 User Profle Clusterng The clusterng problem s defned as parttonng the gven set of users U={u 1,,u n } nto subsets of set U accordng to some optmzaton crteron. We can dstngush three maor types of clusterng algorthms [3]: herarchcal, Eucldean or smlar metrc space and smlarty matrx. The most popular are those algorthms that belong to the famly of Eucldean or smlar metrc space clusterng. Then the clusterng optmzaton crtera could be descrbed as fndng such partton of the set U nto p dsont subsets C =1,..,p of users such that the dstance among all the members of each class - d(c ) s mnmal: = r r = 1 k= 1 k, d( C ) d( u, u ), where r=card(c ), or the dstance between all p groups as stated below s maxmal: d ( U ) = 1 = 1 d ( C, C ) = p where p = 1 q r l= 1 k= 1 l k d ( C, C ), d( u, u ) and r=card(c ), q=card(c l ). The analogous crtera for the smlarty functons may be also shown. Computaton complexty of the problem of partton set U wth n users nto two clusters s exponental wth respect to n. So, practcally other sub-optmal, however more effectve, algorthms should be used. They usually are based on the selecton of some ntal partton as for example n the Dattola method [2], presented below: k, Start In the begnnng we dvde the set U={u 1,,u n } of n users nto k ntal classes: C 1,1, C1,2,.., C1,k. For all of the classes we calculate the centrod. The centrod of the class C s denoted as O =( o,1,o,2,.., o,m } where, o, s where b s a pror assumed constant 0 f = b r, s f, s = 0 otherwse 55

5 f r, s, s = u, s u C { f, t : t = 1,2,.., m} f, s = 1+ max Then n t-1 teraton we have the partton C t-1,1, C t-1,2,.., C t-1,k wth adequate centrodes O t-1,1, O t-1,2,.., O t-1,k. Let T be the threshold used to construct new classes C t, that s determned n the followng way: C t, a : s( a, Ot 1, ) T and = { s( a, Ot 1, ) = max s( a, Ot 1, l ) for l = 1,2,.., k} All the user profles that where not ncluded nto any class should be nserted nto set L t of solated obects. New centrodes are determned as descrbed above f the followng condton s fulflled: s( a, Ot ) > s( a, Ot ),, 1, a Ct, a Ct, otherwse O t, := O t-1,. Users from the set of solated obects L t. The teraton ends when for partcular t and all =1,2,..,k occurs O t, = O t-1,. Then the obects from L t are treated as a separate class or oned to those classes to whch they are the most smlar. End. The other way to solve the clusterng problem, qute smlar to Dattola algorthm s so-called Lloyd s algorthm [5] and s one of the most popular solutons to the k-means problem. The Lloyds s algorthm has the followng steps. Frst, select randomly k elements as the startng centers of the clusters (centrodes). Second, assgn each element of the set to a cluster accordng to the smallest dstance to ts centrod. Thrd, recompute the centrod of each cluster, for example the average of the cluster s elements. Fnally, repeat steps 2 and 3 untl some convergence condtons have not been met (for example centrods do not change). Ths algorthm s rather smple and has an ablty to reach the end when usng the above mentoned convergence condton and for confguratons wthout equdstant elements to more than one centrod, t takes a long tme to run. Frst, the step 2 that has to be performed n each teraton costs O(kdN ), where d s the dmenson of each element and N s the number of elements. Second, algorthm usually needs many teratons to termnate. There are however qute many modfcaton of ths algorthm that run faster, for example bsectng k-means, that begns wth sngle cluster contanng all the elements, then splts t n two clusters and replaces t by splt clusters. Both presented here algorthms were appled n recommender systems, whch mplementatons wll be descrbed n the secton 4. 3 Consensus-based user nterface recommendaton Consensus theory has ts general orgns n the socal scences and n the theory of choce n partcular [9]. The socal choce theory consders problems of analyzng a decson between a collecton of alternatves made by a collecton of dfferent voters wth separate opnons and the selected choce should reflect the desres of all the ndvdual 56

6 voters to the possble extent [16]. The man dfference between consensus theory and the choce theory s that the former one does not necesstates the soluton belongng to the set of opnons under consderaton. In ths secton the model of consensus and hybrd recommendaton s presented. 3.1 Model of consensus In the consensus model we dstngush the followng elements: conflct system, conflct profles and conflct determnaton. Wthn ths model t s assumed that a real world doman s descrbed by means of a fnte set A of attrbutes and a set V of attrbute elementary values [9]. Furthermore let B A and a tuple of type B s a functon r B : B Π(V B ) where ( b B)(r b V b ), and the set of all tuples of type B s denoted by TYPE(B). In the scope of the consensus-based hybrd recommendaton of a web-based system user nterface, as the source of opnons we may assume each clent n the dstrbuted system, that could be also called agents, or dfferent events of the specfed user clent. The subects of agents' nterest consst of event s occurrng n the world,.e. manly observng user behavor, nterface settngs and ts usablty values. These observatons are called events and are stored as attrbute values n a tuple of some type. The conflct system defnton s a modfcaton of the one presented n the work [10]. Here the noton of the category s ntroduced for dfferentaton of the recommendaton type: demographc, collaboratve and content -based. The second dfference reles on the dentfcaton of the nformaton sources. In the conflct system defned n [9] agents are the source of dfferent (conflct) nformaton. The conflct system defned here as the source of conflct nformaton dentfes not only separate agents but also dependng on the category also dfferent events from the gven agent. We can dstngush some subset T A that contans attrbutes for event dentfcaton. Defnton 1 A conflct system of some category c s a quadruple: S c = (A, X, P, Z), where: A s a fnte set of attrbutes (as defned above), ncludng attrbutes that dentfy each event; X s a fnte set of conflct carrers, X ={Π(V a ): a A}; P s a fnte set of relatons on carrers from X, each relaton s of some type L (for L A and L contans attrbute or attrbutes that enable to dentfy the observaton from set T L); Z s a fnte set of logc formulas for whch the model s a relaton system (X,P). Relatons from the set P are classfed n such a way that each of them ncludes relatons representng smlar events. Here these observatons and events concern the dfferent aspects of the user model. A conflct stuaton for a gven category c of the conflct system S c contans nformaton about a concrete conflct as defned below. Defnton 2 A conflct stuaton of a gven category c cs c s a par <P, Y B>, where Y s a set of attrbutes that have nfluence on the nterface settngs: Y L\T and B L\T and Y B= and r Y θ for every tuples r P. A conflct stuaton conssts of event dentfers (conflct body) whch appear n relatons P (conflct content) representng the observed (or nduced) knowledge of referrng to subects represented by set B of attrbutes, n ths case nterface settngs. Expresson Y B means that n the observed events there are dfferences referrng to 57

7 combnatons of values of attrbutes from Y wth values of attrbutes from B, and the purpose of the consensus choce s that for a tuple type Y at most one tuple of type B should be assgned. For a gven stuaton cs c, we determne the set of events whch take part n the conflct as the proecton of the set of relatons P to the set of attrbutes K, Event(cs c ) =? K (P), where K A, and K s a key of relaton P. The set of subect elements (or subects for short) s defned as the proecton of the set of relatons P to the set of attrbutes Y: Subect(cs c ) =? Y (P) where Y L\T. Then for each subect e Subect(cs c ) let us determne set wth repettons Profle(e) whch nclude knowledge from events on subect e Subect(cs c ), as the set of relatons that dentfy the gven subect e reduced to the set of attrbutes B K of for and they are ncluded Profle(e)={r B K : (r P) (e p r A )}. The defnton of consensus s based on the defnton gven n [9]. Defnton 3 Consensus on subect e Subect(cs c ) of stuaton cs c =<P,Y B> s a tuple (C(cs c,e)) where C(cs c,e) TYPE(Y B) that fulfls logc formulas from set Z and one of the followng postulates are fulflled: knowledge closure and consstency, Condorcet consstency condton for choce and convergence condton. The followng theorem should enable to determne a consensus that satsfes all the postulates from the above defnton. The proof of ths theorem s some modfcaton of the one presented n the work [9]. Theorem 1 If there s a defned dstance functon d between tuples of TYPE(B), then for a gven subect e of stuaton cs c =<P,Y B> tuple C(cs c,e) whch satsfes condtons of c Defnton 3 and mnmze the expresson δ ( r, C( cs, e) ) should create a consensus satsfyng all postulates from the defnton 3. r Profle ( e) When all the attrbutes from the user profle are ndependent then the consensus determnaton n the Profle(e) s reduced to the determnaton of consensus for each attrbute n the tuple of TYPE(B). Then dependng on the mcrostructure of attrbute values such as 1-element sets or sets of values, and macrostructure of ther unverse (dstance functon defnton) dfferent algorthms for consensus determnaton could be dstngushed. In case of the smplest mcrostructure when an attrbute a A s represented by 1- element sets of values from some set V a, the consensus determnaton n the profle s based on the consensus choce functon from the Theorem 1. In case of other mcrostructures such as number ntervals, rankngs and sets the algorthms for consensus are more complcated and they can be found n work [10]. Some examples of the conflct systems, of Event(cs c ), Subect(cs c ) and Profle(e) for each category, as well as consensus determnaton wthn the profle wll be shown n the followng sectons. 3.2 Consensus -based recommendaton methods The frst consensus-based recommendaton method was presented n prevous works [10] and [14]. Ths method of the user nterface recommendaton was based on the B B 58

8 hybrd approach that was the mxture of the demographc and the collaboratve recommendaton wth some components of the content -based approach. The system adaptaton (see fgure 1) starts wth regsterng each new user. The regstraton data are stored n the user profle. Then accordng to the user profle the user s assgned to the approprate group. Wth each group of users there s assocated a correspondng nterface profle. The user regstraton s not oblgatory but n ths case the default nterface profle s delvered. Accordng to the nterface profle the actual user nterface content, layout and structure s generated. The user may start to work wth the system and f he or she wshes Fgure 1. Archtecture of the consensus-based user nterface adaptaton [14] also modfy the nterface settngs. Fnally, these settngs together wth usablty evaluaton gven by user are stored n the nterface profle. When the system regsters requred number of users, frst users are clustered usng Dattola algorthm and then accordng to these clusters usng consensus methods new nterface profles for recommendaton are dstngushed. These procedures may be repeated from tme to tme n the followng cases: many new users regsterng to the system or nterface recommendatons become poor usablty ratngs. The second method of the consensus-based hybrd recommendaton usng demographc, collaboratve and content-based approaches appled to dfferent components of the user model. In ths paper we wll only gve a short descrpton of the three types of recommendaton, the more precse one may be found n [15]. 59

9 In the user profle represented by the set of attrbutes A we can dstngush the followng subsets: the demographc attrbutes set D, the demographc attrbutes of the centrod classes of users set N (N D), the set of recommended nterface settngs I, the set of the nterface settng attrbutes made drectly by the user J, the set of the actual nterface settng attrbutes together wth usablty valuaton values F, the set C of attrbutes assocated wth the content (for example vsted pages, purchased or ordered tems, retreved elements) and fnally, we can dstngush some attrbutes used for dentfcaton and authorzaton purposes T. So the set of attrbutes equals the sum of ts elements: A=D J I C T. Each of the determned centrods has the correspondng nterface settngs, whch s assgned by the expert and s recommended to the user after regsterng to the system by delverng demograp hc nformaton (enterng values of attrbutes from the set N). In the mplementaton descrbed n work [14] we consdered only sngle expert opnon, we can assume however that more than one expert opnon s allowed and then determne consensus among all the opnons. In case of the multple expert opnons we can dstngush two stuatons. In the frst one, all the experts share the same centrod attrbute values (concernng demographc attrbutes) but gve dfferent opnons on nterface settngs. In the second stuaton, all the experts gve opnons on centrods settngs and correspondng user nterface attrbutes values. In both cases, however, we try to fnd consensus for each dstnct centrod. Concernng the second case, the number of centrod may ncrease sgnfcantly. When they outnumber the desred lmt we can group them, fnd new centrods and then fnd the consensus wthn these groups. The applcaton of collaboratve recommendaton s possble when sgnfcant number of users have been regstered, used the system, personalzed the nterface and delvered ranked the nterface usablty. More precsely we must have the group of smlar users G concernng values of demographc attrbutes from the set D. The user groups are dentfed by the centrods, determned by the user clusterng descrbed above. For each group correspondng nterface settngs are entered nto the same consensus profle and then the consensus s determned by fndng the values of the nterface settngs for whch: r Profle( e) c r δ ( r, C( cs, e) ), usablty s mnmal, where attrbute set B=J\{usablty}. We should notce that comparng to the expresson from the Theorem 1 the dstance s multpled by the value of the usablty of each partcular nterface. To delver content-based recommendaton for a partcular user we must have suffcent usage data of that user and approprate nductve rules that wll transform ths data nto the user nterface settngs. The rules for effcent content-based recommendatons strongly depend on the goals of the web based system. For example for web-based nformaton retreval systems we can consder the prevous relevant tems as a bass for recommendaton of further retrevals. In ths case many dfferent methods can be used: fuzzy retreval, Bayesan networks or other ntellgent nformaton retreval method. For qute many systems however, the logc used for the retreval systems, does not hold. So, for each recommended tem, no matter f an element of nterface settngs or a content tem, we shall defne precse relatonshp between the user profle (or also other users profles) and ths partcular tem, whch may be mplemented for example as ruled based system, Bayesan or neural networks. However the usage data may lead to many dfferent recommendatons so there s a place for consensus determnaton. B B 60

10 Besde above mentoned recommendatons: demographc, collaboratve and contentbased, we should also menton other ones, such as: envronment, stuaton or emoton based. Ths knd of recommendatons may be dealt n two dfferent ways. The frst one s based on the expanson of the subect s attrbute set wth the attrbute concernng platform, stuaton or emotons n standard collaboratve or content consensus-based recommendaton. The second method treats these recommendatons as separate ones. The consensus system for that knd of recommendaton s smlar to the collaboratve one. However despte groupng users accordng to the demographc features we can group them accordng other attrbutes descrbng computer envronments, stuaton of use, etc. The result of consensus determnaton n all the categores: demographc, collaboratve, content -based, envronment or stuaton based s a recommendaton of the user nterface for the partcular user. Obvously there can be sgnfcant dfferences n the recommendaton of each user nterface attrbutes. So the queston arses, whch type of recommendaton should be preferred? In the cases when only one type of recommendaton s avalable and the others do not delver any (null value of the recommendaton) then t s obvous that we should use the one that s avalable. However usually three or more types of recommendaton may delver dfferent settngs. In such cases specfc selecton rules should be appled. Some examples that descrbe several mplementatons of web-based systems user nterface and content recommendatons, wll be gven n the followng secton. 4 Implementatons of web-based hybrd recommender systems In ths secton we wll present sx selected web-based recommender systems, frst n the subsecton 4.1 three systems that are based on the combnaton of demographc and collaboratve flterng and then n the subsecton 4.2 the other three systems that are based on the complete hybrd recommendaton. The frst car nformaton system [14] was mplemented by M. Wehberg wthn hs master thess work supervsed by the author of ths paper, who also carred out seres of tests that were necessary to verfy recommendaton method. The other systems were mplemented by students of the course Interactve web-based nformaton systems desgn curred out n the academc years 2003/2004 and 2004/2005 supervsed by the author of ths paper. The applcaton domans of recommender systems were followng: wndsurfng, dgtal cameras (two systems), notebooks, computer news, moves (two systems), cookng, motor-bkes, moble phones (two systems) and CD s wth musc (two systems). Here we wll present fve of them appled n the followng areas: har-dressng, moble-phones, cookng, moves and computer news. 4.1 Implementatons usng demographc and collaboratve flterng methods In the car model nformaton system that was started n 2003 we have chosen Renault Megane II (c) the model of the car that was awarded wth Car of The Year That choce was made n order to attract as many users as possble and also because of the ease of acqurng all the necessary materals to buld the system. Ths system had no commercal applcaton and was used only n our laboratory. The car model presentaton system s qute smple and so s the user profle. The data stored n the user profle are entered by the users durng the regstraton process. The user data are reduced to only few attrbutes of demographc nformaton and one that characterzes user s nterests. The nterface profle attrbutes that may be personalzed by the user or recommended by 61

11 the system, contan nformaton concernng nterface layout, musc and sounds, nformaton content and usablty factor. The attrbutes values of the ntal centrods for stereotype reasonng were selected by experts so that none of the stereotype had all the extreme (maxmal or mnmal) values and the dstance between consecutve centrods was smlar. The effectveness of the recommendaton was tested n controlled condtons by 75 users that were students of masters degree and postgraduate studes. The users were of dfferent age, gender, educaton, muscal and graphcal taste, preferences concernng the system layout and nformaton content. The tests were conducted n three steps. In the frst step a group of the users regstered themselves and they were assgned to the approprate group accordng to the smallest dstance to the centrods. Accordng to these assgnments correspondng nterface profles were delvered to them. In ths step users were not allowed to personalze ther nterfaces and at the end the users were asked to fll-n the questonnare concernng seven usablty aspects,.e. nformaton content, vsual content, nteracton etc. In the second step a new group of users was asked to regster themselves and personalze ther user nterface accordng to ther preferences startng from the settngs assgned by stereotype reasonng. Then they were asked to assess the general usablty wth four grades scale. At ths pont recommendaton procedures were carred out. In the thrd step the last group of users, as n the frst step, was assgned to the approprate groups accordng to ther dstance to the centrods. Then accordng to these assgnments, correspondng nterface profles ware delvered to the users. Agan after workng wth the system and obtanng all desred nformaton on the car model users were asked to fll-n the usablty questonnare. Comparson of the all the user nterface usablty aspects between results obtaned n the frst step and the thrd step showed that the nterface adaptaton results n recevng hgher scores. The rase of marks was rather small (0.3 n average n the 10 pont scale) but was encountered n all aspects, so user nterface recommendatons delvered by adaptve procedures were better than those delvered by experts, but both were rated very hgh before and 8.72 after the adaptaton. The second system that was also mplemented usng the frst recommendaton method was Have-a-Look! - har-dress nformaton system, by K. Górka and E. Waslewska. The system recommends the followng nterface settngs: layout, font sze and type, text and background color, hnts, sound track and loudness. The user profle attrbutes concern some demographc features such as: age, professon, nterests; and some specfc system doman features such as: reason for usng the system and how often the user changes har-dress style. The recommended user nterface usablty of ths system was tested usng two methods questonnare and user tests that showed that the nterface s easy to use even for not very experenced users and any maor usablty problems were encountered. The thrd system by M. Cesla and A. Sodlak s presentng selected moble phone model. The system recommends the followng nterface settngs: layout, colors, musc and musc loudness. The user profle attrbutes concern only some demographc features such as: age, gender, educaton and lfe-style. The system was also tested usng questonnare and user tests methods. Questonnares showed that mplemented nterface recommendaton delvers better settngs than the ones delvered by experts however user tests showed some usablty problems that should be consdered n a real system. 4.2 Implementatons usng hybrd recommendaton methods The followng three mplementatons used the second hybrd recommendaton method that takes nto account also content-based approach. In The cookng assstant by M. Podyma & M. Sweczak recommender systems, dfferent types of recommendaton were 62

12 used: basc and hybrd ones. The demographc, that basng on the age and gender of the user offers dfferent nterface settngs such as: musc track, volume, font sze and hnts; and content settngs: addtonal nformaton and wne selecton. These settngs may be changed by the users so t s possble to fnd consensus among these settngs and offer them as a new recommendaton for smlar users. Ths mplementaton also delvers stuaton recommendaton that offers recepts for breakfast, lunch or dnner accordng to the daytme. Fnally also content -based recommendaton s appled accordng to the preferences explctly stated by the users concernng preferred cusne and specfed food products. The system usablty was tested wth 12 users usng the questonnare method and the classcal usablty tests [12]. The test proved that the system usablty s satsfactory. In The Moves by F. Luczys & M. Flarska recommender system also dfferent types of recommendaton were used. The system delvers nformaton about moves stored n the system Stopklatka and current cnema repertore from that nformaton system. The user model s ntalzed durng regstraton process, where each user s asked to gve ratngs of selected flms (as n MoveLens system). Then accordng these ratngs each user s grouped accordng to the stereotype reasonng specfc nterface settngs concernng layout, types of cons, color and background are recommended. The user profle settngs concernng dfferent move attrbutes such as: genre, drector, wrtng, musc, cast and cast are changed accordng to the explct user ratngs of flms or specfed attrbutes and mplct user searchng and browsng of moves. The system delvers reach collaboratve recommendaton that concerns: nterface settngs accordng to the changes delvered by the smlar users, sortng the current cnema repertore for unregstered users accordng to the preferences of all the system users and optonally also for regstered users accordng to the preferences of all smlar users, three extra recommended moves accordng to the hghest ranks gven by the smlar users. The content based recommendaton for regstered user that sorts the cnema repertore accordng to the preferences reflected n the user profle. By default the hybrd recommendaton that s mxture of collaboratve and content-based repertore sortng s appled. The conducted usablty tests contanng the questonnare method and the classcal usablty tests showed some mnor problems wth nteracton but most of the users apprecated mplemented recommendaton mechansms. Fnally, the smplest system Comp News by L. Slwko & K. Jankowsk recommends computer news. At the begnnng users delvers some demographc data, preferred nterface style and ther nterests on four topcs: purchase optmzaton, overclockng, hardware moddng and new products, n form of a feature vector. Then news s sorted accordng to the smlarty wth the nterest vector, whch s subect to constant changes accordng to readng consequent news. The user may also select an average nterest vector determned for the group of smlar users. Even that smple approach was assessed by the expermental users as beng pretty useful. 5 Summary In ths paper only sx hybrd recommendatons mplementatons were shown. However, many other web-based recommender systems were mplemented by students of the course Interactve web-based systems desgn and masters works n recent two years, provng that t s possble to mplement hybrd recommendaton n many dfferent areas. In these mplementatons the consensus methods were usually used n the collaboratve recommendaton, but t s also possble to apply the consensus methods for demographc, content-based and also combned hybrd recommendatons. It s qute dffcult to test recommender systems, especally n controlled condton, because many dfferent users are necessary to show how collaboratve method operates, as well as each user needs rather long tme of workng wth the system to show how 63

13 content-based method operates. However all of the systems were tested wth tens of users (one almost 80) and some met hods were tested wth most exhaustve user tests method, whch methodology suggest to test only fve users. These tests showed the ncrease of some usablty factors after applcaton of system recommendaton. References [1] Dastan M, Jacobs N, Jonker CM, Treur J, Modellng User Preferences and Medatng Agents n Electronc Commerce. LNCS 1991, 2001, pp [2] Dattola RT, A fast algorthm for automatc classfcaton. Report ISR -14 to the Natonal Scence Foundaton, Secton V, Cornell Unversty, Department of Computer Scence, [3] Estvl-Castro V, Yang J, Categorzng vstors dynamcally by fast and robust clusterng of access logs. LNAI 2198, 2001, pp [4] Internatonal Standard ISO , Ergonomc requrements for offce work wth vsual dsplay termnals (VDT s) Part 11: Gudance on Usablty, [5] Kanungo T, Mount DM, Netanyaho NS, Patko C, Slverman R, Wu AY, An Effcent k-means clusterng algorthm: analyss and mplementaton. IEEE Trans. On Pat. Analyss And Mach. Intell., 24 (7), 2002, pp [6] Kobsa A, Koenemann J, Pohl W, Personalzed Hypermeda Presentaton Technques for Improvng Onlne Customer Relatonshps, The Knowledge Eng. Revew, 16(2), 2001, pp [7] Montaner M,. Lopez B, de la Rosa JL, A Taxonomy for Recommender Agents on the Internet, Artfcal Intellgence Revew, 19, 2003, pp [8] Newman WM, Lammng MG, Interactve system desgn, Addson-Wesley, Harlow, [9] Nguyen NT, Consensus System for Solvng Conflcts n Dstrbuted Systems, Journal of Informaton Scences, 147, 2002, pp [10] Nguyen NT, Sobeck J, Usng Consensus Methods to Construct Adaptve Interfaces n Multmodal Web-based Systems, J. of UAIS., 2(4), 2003, pp [11] Papatheodorou C, Machne Learnng n User Modelng, Machne Learnng and Its Applcatons, 2001, pp [12] Pearrow M, Web Ste Usablty Handbook, Charles Rver Meda, [13] Rsbergen C, Informaton Retreval. Butterworths, London, 2nd edton, [14] Sobeck J, Wehberg M, Consensus-based Adaptve User Interface Implementaton n the Product Promoton, n Keates S (et al.), Desgn for a more nclusve world, Sprnger-Verlag, London, 2004, pp [15] Sobeck J, Consensus-Based Hybrd Adaptaton of Web Systems User Interfaces, Journal of UCS, 11(2), 2005, pp [16] Socal Choce Theory, SocalChoceTheory.html, onlne Oct [17] Whalen D, The Unoffcal Cooke FAQ, Verson 2.54 Contrbuted to Cooke Central by Davd Whalen, onlne