2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Web Intelligence in Tourism User Modeling and Recommender System runo co o Martins GECAD-Knowledge Engineering GECAD-Knowledge Engineering GECAD-Knowledge Engineering and Decision Support Group and Decision Support Group and Decision Support Group Porto, Portugal Porto, Portugal brunocoelho(@dei isep. pp pt const(@dei isep. ipp.pt ana(@dei isep. tpp. pt Abstract-This paper presents a successful attempt at evolving web brief approach regarding UM in tourism, while section 3 refers ss, which is divided in three stages: the user areas: User modeling and Recommender systems. The first model itself, the set of reasoning components and the user a wide variety of techniques, such as stereotypes, keywords and customization stage. In section 4, the developed tourism psychological models. These techniques, besides presenting user application will be presented regarding its advantages and interests with great coherence and completeness, allow for the testing results. Finally, in section 5 some conclusions will be reduction of several current problems such as the cold start issue, outlined, along with future work perspectives gray sheep individuals and overspecialization. The recommender system, by making use of all user models'building IL. USER MODELING IN TOURISM n interesting, innovative and hybrid nature to as behavioral filtering, multi-techniq Despite recent evolutions in UM, current systems still do resourcefulness and on-the-fly suggestions. The architecture was not explore the huge potential of this area, namely in the already tested in the scope of a prototype regarding the city of tourism domain [18J[19]. User information is extensively Porto, in Portug requested at startup, without necessarily being used throughout the remaining life-cycle of the application. Most of Keywords-user modeling: user-adapted representations of the assumptions they make about users, which ends up in incoherent recommendations being . INTRODUCTION performed B3[15]. [61, [9], [18 and [19] present some he tourism domain, and in particular, the holiday choosing interesting systems which use UM techniques and / or are process, represent very complex decision-making matters. On pertinent to the domain at hand. Since the data available is not one hand, the user is faced with the obviously endless group of very extent, it was not chosen to perform a formal comparison existing options, and on the other, the heterogeneity of the between the different systems. Still, informal considerations places to visit. Furthermore, he still has to consider the can be extrapolated. Some of the referred systems [6]follow a inherent specificities of the holiday chosen destiny, like, for knowledge-approach, by making use of several kinds of example, the type of Points of Interest(POI)available, hosted knowledge management techniques(suppositions and beliefs, events and so on. Another reason that explains why this for instance), which, although dealing with certainty in process is so difficult is the fact that, besides user's own inferred data, are much more computational intensive. These interests and preferences, many times they also have to take techniques are too strict when we consider the final natural into consideration those of other people as well [5] task of UM application: the rs. The rs needs an extremel iven those premises, tourism is an area clearly electable well balanced relation between fast and reliable data, which is for the use of Artificial Intelligence(Al) and its benefits, and not achieved when using those complex knowledge-based in particular, Decision Support Systems(DSS)[1]. Such techniques. The use of stereotypes was positively detected systems require the use of a coherent model of the user in these applications [6], and will be an integral part of the work order for results, and the overall system, to be customized and to be described In a very broad statement, the current main targeted to him / her: thats where User Modeling(UM) flaw regarding tourism systems is the poor UM backing them techniques come into play [2]. After an assessment of the up. Most systems rest their efficiency on a single Ul current tourism platforms, as well as general systems which technique; even if such modeling is not incorrect,it Ise UM, there is the belief that significant work can still be certainly not enough, considering the complexity of the human done, regarding a more complex modeling of users and being in various aspects, such as behavioral. Another approach uch more useful and effective use of those models surfacing in the latest years is the overrated preference for 978-0-7695-4191-4/052600◎2010IEEE DOII0.I109/I-AT2010236Web Intelligence in Tourism User Modeling and Recommender System Bruno Coelho GECAD - Knowledge Engineering and Decision Support Group Porto, Portugal brunocoelho@dei.isep.ipp.pt Constantino Martins GECAD - Knowledge Engineering and Decision Support Group Porto, Portugal const@dei.isep.ipp.pt Ana Almeida GECAD - Knowledge Engineering and Decision Support Group Porto, Portugal ana@dei.isep.tpp.pt Abstract-This paper presents a successful attempt at evolving web intelligence in the tourism scenario, namely throughout two main areas: User Modeling and Recommender Systems. The first subject deals with the correct modeling of tourists’ profiles using a wide variety of techniques, such as stereotypes, keywords and psychological models. These techniques, besides presenting user interests with great coherence and completeness, allow for the reduction of several current problems such as the cold start issue, gray sheep individuals and overspecialization. The recommender system, by making use of all user models’ building blocks, brings an interesting, innovative and hybrid nature to the area, with benefits such as behavioral filtering, multi-technique resourcefulness and on-the-fly suggestions. The architecture was already tested in the scope of a prototype regarding the city of Porto, in Portugal. Keywords-user modeling; user-adapted web systems; recommender systems; stereotypes; tourism I. INTRODUCTION The tourism domain, and in particular, the holiday choosing process, represent very complex decision-making matters. On one hand, the user is faced with the obviously endless group of existing options, and on the other, the heterogeneity of the places to visit. Furthermore, he still has to consider the inherent specificities of the holiday chosen destiny, like, for example, the type of Points of Interest (POI) available, hosted events and so on. Another reason that explains why this process is so difficult is the fact that, besides user’s own interests and preferences, many times they also have to take into consideration those of other people as well [5]. Given those premises, tourism is an area clearly electable for the use of Artificial Intelligence (AI) and its benefits, and in particular, Decision Support Systems (DSS) [1]. Such systems require the use of a coherent model of the user in order for results, and the overall system, to be customized and targeted to him / her: that’s where User Modeling (UM) techniques come into play [2]. After an assessment of the current tourism platforms, as well as general systems which use UM, there is the belief that significant work can still be done, regarding a more complex modeling of users and a much more useful and effective use of those models. This paper is organized as follows: section 2 will present a brief approach regarding UM in tourism, while section 3 refers to the UM process, which is divided in three stages: the user model itself, the set of reasoning components and the user customization stage. In section 4, the developed tourism application will be presented regarding its advantages and testing results. Finally, in section 5, some conclusions will be outlined, along with future work perspectives. II. USER MODELING IN TOURISM Despite recent evolutions in UM, current systems still do not explore the huge potential of this area, namely in the tourism domain [18][19]. User information is extensively requested at startup, without necessarily being used throughout the remaining life-cycle of the application. Most of the times, though, systems rely on single and / or poor representations of the assumptions they make about users, which ends up in incoherent recommendations being performed [3][15]. [6], [9], [18] and [19] present some interesting systems which use UM techniques and / or are pertinent to the domain at hand. Since the data available is not very extent, it was not chosen to perform a formal comparison between the different systems. Still, informal considerations can be extrapolated. Some of the referred systems [6] follow a knowledge-approach, by making use of several kinds of knowledge management techniques (suppositions and beliefs, for instance), which, although dealing with more certainty in inferred data, are much more computational intensive. These techniques are too strict when we consider the final natural task of UM application: the RS. The RS needs an extremely well balanced relation between fast and reliable data, which is not achieved when using those complex knowledge-based techniques. The use of stereotypes was positively detected in these applications [6], and will be an integral part of the work to be described. In a very broad statement, the current main flaw regarding tourism systems is the poor UM backing them up. Most systems rest their efficiency on a single UM technique; even if such modeling is not incorrect, it is certainly not enough, considering the complexity of the human being in various aspects, such as behavioral. Another approach surfacing in the latest years is the overrated preference for 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 978-0-7695-4191-4/10 $26.00 © 2010 IEEE DOI 10.1109/WI-IAT.2010.236 619