a design of semantic-based recommender system for...

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A DESIGN OF SEMANTIC-BASED RECOMMENDER SYSTEM FOR MEDICAL TOURISM

Anindhita Dewabharata1, Shuo-Yan Chou1, Febriliyan Samopa2

1Department of Industrial Management, School of Management, National Taiwan University of Science and Technology

No.43, Sec. 4, Keelung Road, Da'an District, Taipei City 106, Taiwan (R.O.C) Phone: +886-2-2737-6341, Fax: +886-2-2737-6344

2Information Systems Department, Institut Teknologi Sepuluh Nopember Jl. Raya ITS, Keputih, Sukolilo 60111

Telp: (031) 5999490, Fax : (031) 5964965 E-mail: [email protected]

Abstrak

Pariwisata medis telah berkembang pesat dalam beberapa tahun terakhir. Kecenderungan ini menyebabkan informasi tentang tujuan pariwisata medis akan meningkat secara signifikan. Informasi dari pariwisata medis telah ditemukan secara online karena adanya penyebaran demografis dari wisatawan medis dan tujuan pariwisata medis yang potensial.Namun, pertumbuhan informasi yang tersedia di web saat ini telah menyebabkan informasi yang berlebihan, menghambat kemampuan pengguna untuk membedakan informasi yang relevan dari yang tidak relevan.Kondisi ini membatasi un-tuk menggunakan sumberdaya informasi secara efektif.Karena fakta ini, sistem rekomendasi telah mendapatkan momentum sebagai alat yang efisien untuk mengurangi kompleksitas dalam mencari infor-masi yang relevan.Kemampuan personalisasi sungguh berharga bagi sistem rekomendasi untuk menco-cokkan preferensi pengguna terhadap semua sumberdaya pariwisata medis.Dalam merancang sistem rekomendasi, penting untuk mempertimbangkan tentang keputusan pembangunan desain utama dan di-batasi oleh lingkungan yang mempengaruhi sistem rekomendasi.Sistem rekomendasi dirancang dengan menggunakan teknologi web semantik untuk memodelkan wilayah pengetahuan dan sebagai teknik rek-omendasi berbasis konten.Akhirnya, dalam penelitian ini diajukan sebuah desain dari sistem rekomen-dasi untuk pariwisata medis.Sistem ini akan memberikan rekomendasi terkait semua sumberdaya pari-wisata medis dalam satu paket kepada pengguna

Abstract

Medical tourism has been growing rapidly in recent years. This trend causing the information about medical tourism destination will increase significantly. The information of medial tourism has been found online started from the demographic spread of the potential medical tourists and destination. How-ever, the growth of information available on the web nowadays has led to information overload, hamper-ing the user's ability to distinguish relevant information from irrelevant. This condition restricts people use information resource effectively. Due to this fact, recommender systems have gained momentum as an efficient tool to reduce the complexity when searching for relevant information. Personalization capabili-ties are valuable for recommender system to match the user's preference against all medical tourism re-sources. In designing a recommendation system, it is important to consider about construction of the main design decisions and must be constrained by the environment of the recommender which is influence them. The recommender system is designed by using the technology of the semantic web to model the do-main knowledge and as a content-based recommendation technique. Finally, a design of recommender system for medical tourism has been proposed in this research. The system will generate recommendation of medical tourism resources all in one package to users. Keywords: medical tourism, recommender system, semantic web, ontology, semantic association.

Jurnal Sistem Informasi, Volume 4, Nomor 3, September 2012, hlm 143-156

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1. INTRODUCTION

Medical tourism is simply the outsourcing of medical services, describes the phenomenon of people traveling across international borders, especially from developed countries to develop-ing countries, to obtain health care.Strong eco-nomic development in developing countries has provided the resources and opportunities to build massive health care centers for patients traveling from all around the world.With these trends, interest in developing tourism related to the medical industry has increased globally, and medical tourism is now marketed as a niche product that encompasses both medical services and tourism packages (Connell, 2006). The basics of medical destination has been found online started from the demographic spread of the potential medical tourists, and because of the growth of medical travel that have been advanced and has developed a next stage using the internet as a source of travel information. The internet was an information source to other travel information sources for the majority of consumers searching for travel purposes (Oorni, 2004). The explosive growth and variety of infor-mation available on the Web is becoming in-creasingly severe in modern times, and lead to information overload, hampering the user's abil-ity to distinguish relevant information from ir-relevant. Personalization capabilities are un-doubtedly valuable in medical tourism because there are many options of destination. Recom-mender systems have been gaining momentum as another efficient means of reducing complex-ity, by matching the users' preferences (modeled in personal profiles) against all available tour-ism destination resources (Blanco-Fernández et al., 2010). Semantic web is an extension of the current web in which information is given well defined meaning, better enabling computers and people to work in cooperation. The proliferation of semantics-based recommender systems has re-vealed important limitations related to the per-sonalization capabilities offered by the systems, their process of user profiles initialization, and the semantic matching techniques adopted by the recommendation strategies to compare the users’ preferences against the metadata of avail-able resources (Blanco-Fernández et al., 2010).

The primary objective of this research is design-ing a model of semantic-based recommender system for medical tourism. The recommenda-tion provided information consists of the medi-cal tourism resources needed by users. For rec-ommender systems, it is important to afford personalized advice for users regarding items or services they might be interested in. Related to personalization, there are four main components to be acknowledged, a database where the avail-able items are stored, personal profiles where the users’ preferences are modeled, the recom-mender may also have knowledge about the features of the items that is recommended and the strategies aimed at selecting personalized suggestions for each user. In order to fulfill the-se needs, it is using technology borrowed from semantic web with the plan to semantically at-tach pieces of information in order to lighten the users’ burden of finding, understanding and explore tourism-related information sources. The design of the recommender system is trying to comply with the purpose of the semantic web, which are a language for recording how the data relates to the real world objects about common formats for integration and combination of data drawn from diverse sources, where on the origi-nal web mainly concentrated on the interchange of documents. In order to make workable choices when initial-ly designing the system, in a logical way, it is useful to consider about construction of the main design decisions and the factors which influence them. Designing a recommender sys-tem means making choices of technique, archi-tecture and user profile (Picault et al., 2011). Technique is about which recommendation methods to use. Architecture shows how will the system be deployed e.g. centralized or dis-tributed, and User profile is related with type of the user modeling and its conditions. These choices can be constrained by the environment of the recommender. The environment is de-scribed into three dimensions, which are users, data and application. 2. METHODOLOGY

The main purpose of this section cover theoreti-cal framework from some literatures related on this research. The literatures are about medical tourism, semantic-based recommendation sys-tem, ontology and semantic association.

Dewabharata dkk., A Design of Semantic-Based Recommender System for Medical Tourism

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2.1 Medical Tourism Medical tourism has grown dramatically in re-cent years primarily because of the high costs of treatment in rich world countries, long waiting lists (for what is not always seen institutionally as priority surgery), the relative affordability of international air travel and favorable economic exchange rates, and the ageing of the often af-fluent post-war baby-boom generation (Connell, 2006).There are privacy issues for some patients when performing health care procedures in their own country (Vitalis and Milton, 2009). The idea to combine an exotic vacation spot along with a medical procedure looks interest-ing to many people (Connell, 2006). Several employers and insurance companies have en-dorsed institutions in other countries for treat-ment overseas as a part of financial savings. The growth of the Internet could provide access to information via online to the hospital site, a travel agency that specializes in arranging tours of medical, patient's last blog (Altin et al., 2011). All of these conditions can be enabling factor for travelers in making decisions. Deci-sion to travel overseas especially in looking for medical treatment is difference to the decision to purchase goods or services. Decision to travel in search of medical treatment is very involved, complex, multi-faceted, and often emotional in nature (Crouch and Louviere, 2001). In (Altin et al., 2011) has described a model of the travel-er’s decision components involved for medical tourism as shown in Figure 1.

2.2 Semantic-based Recommender System An effective solution for reducing complexity when searching information over the Internet has been given by recommendation systems (Adomavicius and Tuzhilin, 2005).A personalization system is based on three main functionalities: content selection, user model adaptation and presentation of results (Diaz et al., 2008). Selecting destination, tourist attract-ions, accommodations, or all the above for planning a whole trip refers to content se-lection.Lately personalized recommendation systems have been gaining interest in tourism to assist users with their travel plans (Rabanser and Ricci, 2005, Ricci, 2002). Recommender systems have been used successfully in travel and tourism. For example, Ricci (2002)identifies TripleHops TripMatchers used by www.ski-europe.com and VacationCoachs used by Travelocity.com. Syntactic limitations of the traditional appro-aches can be avoided by resorting to reasoning techniques, borrowed from the Semantic Web. This initiative is based on: (i) annotating Web resources by metadata, (ii) formalizing this knowledge in domain ontology, and (iii) apply-ing reasoning processes to infer semantic relat-ionships among the annotated resources (Blanco-Fernández et al., 2008a). They also have developed domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relat-ionships inferred between the user’s preferences and the available items.

Information SearchPre-Decision

Demand Triggers

Cost

Waiting Time and Availability

Privacy and Confidentiality

Destination of Country Profile

Hospital / Treatment Facility

Medical Tourism Service Facilitator

Role of Stakeholder

Travel Experience and Post Travel

Evaluation

Evaluation and Decision

Lower Travel Cost and Lure of a vacation

Insurance Company

Endorsement

Access to information via

internet

Enabling Factors

Figure 1. The traveler’s decision components


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2.3 Ontology In the field of Semantic Web, ontology is a for-mal specification of a conceptualization, that is, an abstract and simplified view of the world that we wish to represent, described in a language that is equipped with a formal semantics. Ontol-ogy characterizes semantics in terms of con-cepts and their relationships, represented by classes and properties respectively. Both entities are hierarchically organized in the conceptual-ization, which is populated by including specific instances of both classes and properties (Blanco-Fernández et al., 2010). For example, in the context of a recommender system, in-stances of classes represent the available items and their attributes, whereas instances of proper-ties link the items and attributes to each other. Currently, OWL is the most expressive lan-guage which is designed for used by applica-tions that need to process the content of infor-mation instead of just presenting information to humans. 2.4 Semantic Association Most useful semantic associations involve some intermediate entities and associations. Relation-ships that span several entities may be very im-portant in domains such as national security, because they may enable analysts to see the connections between seemingly disparate peo-ple, places and events. Semantic association has been used as content-based filtering technique in recommendation system (Blanco-Fernández et al., 2008b). Con-tent-based approach adapts in the OWL ontolo-gy instances of classes and properties that are applicable for the user, by considering their preferences (Blanco-Fernández et al., 2008a). Then, reasoning-based strategy infers semantic association among the entities selected that identify specific items. These hidden associa-tions are found from the hierarchical link and the property is defined in the domain ontology. The semantic associations employed in reason-ing approach have been adopted from Anyanwu and Sheth (2003), who defined the relationships that can be established between two specific class instances in ontology. In order to catego-rize these associations, they resorted to a struc-ture named property sequence, which consists of a set of class instances linked to each other by means of properties. Spreading Activation (SA) techniques will be employing with the semantic association as pro-posed by Blanco-Fernández et al. (2008a). First, the approach extends the simple relationships considered by traditional SA techniques by con-

sidering both the properties defined in the on-tology and the semantic associations inferred from them. These rich varieties of relationships permit to establish links which propagate the relevance of the items selected by the filtering phase, leading to diverse enhanced recommen-dations. Second, to fulfill the personalization requirements of a recommender system, the link weighting process does not depend only on the two nodes joined by the considered link, but also on (the strength of) their relationship to the items defined in the user’s profile. This way, the links of the network created for the user are updated as the strategy learns new knowledge regarding his/her preferences, thus leading to tailor-made recommendations after the spread-ing process. 3. RESULT and EXPLANATION

As mentioned in the methodology, the design of this research will be based on the technology concept borrowed from semantic web, which are: the ontology for modeling the domain knowledge, and semantic association as a con-tent-based strategy. But, before heading to these concepts, at the first need to illustrate how to instantiate the environmental models propo-sed by Picault et al. (2011), in order to deter-mine constraints on the recommender design. The environmental model consists of three dimen-sions, namely: application model, user model and data model, as shows in Figure 2.

3.1 Environmental Model The main factors impact the recommender de-sign regarding the application model comes with two main terms: the role of the recom-mender and the influence of the application im-plementation. Related to application model, Tabel 1 describes some features of the recom-mender system designed. User is basic compo-nent of any recommender system. There three things that need to understanding form the us-ers, which are: understanding their key identify-ing characteristics, their skill levels and their prior experience with similar systems. The iden-tification of user characteristics can be shown in Table 2. The last of the environmental model is the characteristics of the items (data) the system will exploit and manipulate. Table 3 shows the main characteristics of data may influence the design and the results of proposed recommender system. After finish with the feature from each dimension of environmental model, then con-tinue to develop the semantic concept adapted to the recommender system, starting from domain ontology.


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Recommender System

TechniqueUser profileArchitecture

Users

Data

Application

Environment

Figure 2. The recommender in its environment

Table 1. Application model instantiation

Features Values Purpose Help user to find medical tourism destination by optimize their search-

ing process. Type Multiple items, it provides a list of medical destination resource, such

as medical provider, accommodation, attraction, etc. Integration with navigation features

Tight through coupling with the information retrieval to deliver a per-sonalized search service

Performance criteria Transparency, correctness and serendipity Device to support application Fixed and mobile Numbers of users Single and users that interact with the personalized application must be

registered Application infrastructure Browser-based (client-server) application with pull mode content de-

livery (personalized-search) and combination of semi-distributed com-puting for source data

Table 2. User model instantiation

Features Values Demographic information No demographic information is considered as a discriminatory of the

user. But demographic information will be acquired for user prefer-ences purpose

Explicit goal Expressed through user queries in the retrieval engine Level of expectation Is not too high, the users can still use the system if the recommender

does not work or perform poorly (though with a degraded efficiency). Change of expectation over time

Should increase when they progressively discover the benefits of per-sonalized search functions

Importance of user situation High, users in different locations would also expect different results. Social environment Feedback from other users is necessary application must be registered Trust and privacy concerns Users that interact with the personalized application must be registered

Table 3 Data model instantiation

Features Values Data type Semi-structured: medical tourism resources items are described

through a number of categories and concepts. Quality of metadata Good expressiveness through semantic of content. Description based on stand-ards

Ontology is used for description based on standards

Volume / diversity of items huge number of medical tourism resources items on a large variety of topics

Distribution of items long-tail and mainstream: wide distribution of concepts, from those about medical provider, accommodation and attraction, etc.

Stability and persistence of items

The item set is persistent, as it constitutes a continuous stream, there-fore the items taken into account for recommendations are changing

User ratings Explicit ratings (such as star rating) are considered


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3.2 The Domain Ontology The primary entities of the domains knowledge in recommender system, users and feature of item, will be represented by ontology. This rep-resentation will describe a preference between the user and the item, commonly referred as user profile or in the next section will referred as users‘ ontology interest. The users’ prefer-ence is necessary to aim at selecting personal-ized suggestions for each individual. Developing the ontology model for proposed recommender system, need to consider three aspects, which are semantic detail, hierarchical representation and inference mechanism. These aspect has been discussed in (Cantador et al., 2011), and this research will follow their ap-proach. Medical tourism as partof tourism has own characteristic. The characteristic of the medical tourism can be obtained from the trav-eler’s decision component as explained in the section 2.1. The decision component is substan-tial, because it needed to be extracted to offer basic foundation of the users and items’ entity and attribute. Table 4 shows the entities and attributes of the users and items acquired. As shown, some attributes of the user is a context and demographic data, and also some attributes from the items is also a context. This type of data is very useful to offer personalization of recommendation. In order to avoid the ontology grows bigger and then explode, for building the ontology will be limited by using only entity and attribute from Table 4. Table 5 shows the example of a part structure of domain. Figure 3 shows class hier-archy of domain ontology of medical tourism and Figure 4 shows diagram of ontology using OntoGraf model. This hierarchy and diagram is created based on data acquired from Table 4 and Table 5 which is extracted from the traveler’s decision component, combine with the explora-tion from medical tourism website such as allmedicaltourism.com and medretreat.com,and general tourism website such as tripadvi-sor.com. The diagram in Figure 4 shows some relations between medical provider (class name: Medi-calInstitution consist DoctorClinic, Medi-calCenter and Hospitals as subclasses), medical procedure (class name: MedicalProcedure) and Person (representation of user). Some examples of instances can be shown too, such as Apicaoc-tomy, Crowns and Bridges as instances of Den-

tal, and DentalPoland as an instance of Medi-calCenter. The example of object property and inference mechanism can be shown in Figure 5. The ontology is developed using protégé, tools provided by Stanford University and will be deployed with the format of RDF/OWL files. Once deployed, class, property and instance can be manipulated with the help of Jena 2 Frame-work. Table 4. Entities and attributes extracted from travel-er's decision component

Domain Entity Attribute

Destination

Medical Provider

Type, Facilities, Ser-vices, Medical Treat-ment offered, Cost, Waiting Time, Accredi-tation, Location.

Accomo-dation

Type, Rating, Facilities, Services, Availability, Cost, Location.

Trans-portation

Type, Cost, Availabil-ity, Time Duration

Attract-ion

Type, Location, Facili-ties, Services

Destina-tion Country

Location, Weather, Safety, Socio-cultural

Traveler Person

Medical condition, Age, Occupation, In-surance, Marital status, Location, Time Dura-tion, Language

Table 5. Example of structure of domain

Domain Class

Level 1 Level 2 Traveler Person -

Destination

Accommo-dation

Hotel Motel Condominium Inn Home stay Bed and breakfast Guest House

Attraction

Park Water Historic Sites Sports Events


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Figure 3. A class hierarchy of medical tourism ontology

As described in the first paragraph of this sec-tion, the domain ontology provides a formal representation of the users’ preferences. The ontology model named ontology-profile intro-duced by Blanco-Fernández et al. (2008a) being able to reason about users’ preferences and reveal knowledge about their interests. As also explained by Blanco-Fernández et al. (2008a), this domain ontology model does not need defining in each user's profile, the class, properties and instance that identify his/her preference in the ontology. In particular, the domain ontology employed as a general knowledge repository and only save in the us-er‘s profile the instances that were (un)appealing to him/her and. In addition these preferences allow to place in the ontology the

item defined in the user profile and inquiry their semantic over the OWL knowledge repository (Blanco-Fernández et al., 2011). In order to ful-fill the goals of this strategy, the user interest must be recognized in the model and can be obtained explicitly from the criteria given by user as initial preferences and users’ feedback(Blanco-Fernández et al., 2008a). 3.3 Content-based Strategy The medical tourism domain among the purpose of diversifying the recommendations, personali-zation strategy suggests concept that are seman-tically associated with the user preferences (e.g. medical condition, context etc.), disregarding the syntactic similarity metrics adopted in the traditional content-based methods. Basically, there are two phases of content-based strategy, filtering and recommendation phase as shown in Figure 6. Firstly, the filtering phase selects an excerpt from the domain ontology that comprises only instances of classes and properties that are sig-nificant for the user (because they are closely related to his/her preferences), by infer semantic associations among the items included in the user’s ontology of interest. Next, the recom-mendation phase processes the discovered knowledge and provides the personalized rec-ommendations. To meet this aim is by adopt SA techniques as mechanisms able to explore effi-ciently a generic network with nodes intercon-nected by links, and to detect concepts that are strongly related to each other.

Figure 4. A part of diagram of medical tourism ontology


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One of the fundamental processes of recom-mendation systems that should be mentioned to support the filtering phase and the recommenda-tion phase is the information feedback or users’ feedback. User profiles are developed from pos-itive and negative examples of interest, acquired from explicit feedback or heuristics analyzing browsing behavior (Middleton et al., 2001). Explicit feedback will be used in this research. The filtering phase determines which instances of classes and properties from the domain on-tology should be included in the user’s Ontolo-gy of Interest because they are relevant for him/her. For measuring the semantic intensity of a node, in (Blanco-Fernández et al., 2008b) ac-count various ontology-dependent filtering cri-teria are taken, so that the more significant the relationship between a given node and the user’s preference, the higher the resulting value. These criteria are described as follows: 1. Length of the property sequence that enables

to reach the node starting from the user’s preferences.

2. Existence of hierarchical relationships be-tween the node and the user’s preferences.

3. Existence of implicit relationships between the node and the user’s preferences detected by graph theory betweenness.

After identifying the node correspond to the user’s preferences and the properties linking them to each other, and then infers semantic associations between the instances referred to user’s ontology of interest. Based on Blanco-Fernández et al. (2008a), two instances are as-sociated when at least one of the conditions be-low is true: • Sequence of properties in the ontology is link-

ing two instances. For example, Mexico and Argentina (instances of class country) are

linked by a sequence of two properties by the American node (Instance of class continent).

• The same category in the domain ontology is used to classify the attributes of the two in-stances. For example ExperienceAndalusia and ModernArt are related because their at-tributes PaseodelArte and Prado belong to the Museum class.

• In the hierarchy of genres defined in the on-tology, the two instances share a common an-cestor. For example Modern Art and Contem-porary Art belong to the Painting class, under the Art category.

A network for the user has been built by using filtering and knowledge inference process. Through the filtering phase, the instances of classes are chosen as nodes and links are both the properties in the ontology and the semantic association inference from it. This network has the knowledge representation that should be explored during the second phase of this strate-gy.

Figure 5. A small part of object property and inference mechanism

Recommendation phase

Filtering phase

Semantic Association

Users’ ontology interest

Semantic Inference

mechanism

Build Spreading Activation Network

Selection of recommendation

Retrieve content of Recommendation

Content of All Destination Resources

Active User

QueryCriteria

The Recommendation

User positive and negative

preferences

User’s feedback

destination domain

user domain

Figure 6. Content-based flow diagram


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For the recommendation phase will be used Spreading Activation (SA) network proposed in Blanco-Fernández et al. (2008c) as mechanism to efficiently explore the relationship between the concept interconnected in the aforemen-tioned network, and infer from the knowledge useful for the recommendation process.

• Firstly is activating the user’s SA network the nodes referred to the items defined in his/her profile, by considering both his/her positive and negative preferences. The positive prefer-ences permit the spreading process to identify items that are significant for the user, because they are related to items he/she enjoyed in the past. The negative preferences lead to detect items that must not be suggested due to their relationships to unappealing items.

• Secondly is assigning the activation levels of all the nodes in the network by using the in-dexes defined in the user’s profile for the nodes initially activated, and a value 0 for the remaining nodes.

• Last, the activation levels of the user’s prefer-ences are propagated through the SA network that is in charge of selecting the items with high levels to be recommended to the user.

Once all the nodes in the user network have been reached by the spreading process, the highest activation levels correspond to the in-stances filling two condition: their neighbor nodes are also appropriate for the user (that is why their high activation levels) and they are closely correlated to the user’s preferences (that is why the height of the links). For that reason, those nodes recognize the instances finally rec-ommended by this content strategy. As described in the domain ontology, the rec-ommendation requires information feedback from the user as a component of user prefer-ences. The feedback techniques are categorized into two types: Implicit and Explicit feedback (Resnick and Varian, 1997, Adomavicius et al., 2005, Ziegler et al., 2005). The explicit feed-back is the most used in the recommender sys-tem in force, because the user himself whoever value the importance of interest object (Nunez-Valdez et al., 2011). The system is evaluated by giving a score to an individual object or a set of objects during a review process that performed by the users. Then the users would be provided with explicit feedback among with a mechanism to obviously

express their interests in objects. As shown in Figure 7, through the explicit rating “Like”, the users assign a positive or negative rating to con-tents (Jawaheer et al., 2010). For example the user is interesting with the four season hotel, so the system will give positive rating to the four season hotel. Or user also like water activity such us water skiing. The system also gives positive rating to the water skiing. But user doesn’t like with the sunrise motel and the jog-ging modern art exhibition, so the system gives negative rating to both of them. Table 6 shows this example. A simple example of the use of content-based strategy is presented. Assume the user searches destination information of medical tourism, with the criteria of the medical procedure is dental bridges, user’s location is in Canada and user’s language is English. There is no historical trans-action, because the user is new. Ontology used in this example based on the ontology of the figure and only limited to suggest medical pro-vider. In the filtering phase, the potential instances from domain ontology shown in Figure 8, is by selected for user by using the filtering criteria: • The instances CostaMed, DentalPoland, and

Apollonia are selected because all of them are directly related to user’s preferences criteria for dental bridges procedure.

• User’s location is in Canada, for this reason, the instances Mexico and Argentina is select-ed, as it share common ancestor America with the instance Canada.

• English is selected because it is linked to the user’s preferences (language).

Figure 7. Explicit rating “Like” [44]

Table 6. Example of users' preference

Class Subclass Instance Rating

Accom-modation

Hotel The four season

Positive [1]

Motel

Sunrise Motel

Negative [-1]

Sunset Motel

Positive [1]

Attraction

Water Activity

Water skiing

Positive [1]

Painting Modern Art

Negative [-1]


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[1][1]

[1][2] [2]

[2] [2]

[1][1] [3]

[4]

[3]

[3]

[1] hasLanguage [2] hasTreatment

[3] hasCountry [4] hasContinent

Figure 8. Instances selected for the user

After select instances, the strategy infers seman-tic association between the preferences of user and selected Medical Provider. The CostaMed is selected because it is related two of the instanc-es that user prefer, English and Bridges which are linked by a chain sequence of properties in the ontology, hasLanguage and hasTreatment. There is no negative preference discovered, because this is the first time user uses thesys-tem. The next step is recommendation phase. It is not difficult to build the link of the network and spreading the activation level of the user prefer-ences since only one result of medical provider filtered from previously phase, This phase then suggests the CostaMedby by exploiting the se-mantic association existing between it and user preferences. The links referred to this associa-tion in Figure 9authorize SA techniques to spread the relevance of English and Bridges. 3.4 The Prototype of Semantic based Rec-ommender System The prototype concept of the recommender sys-tem will be described in this section. It consists of system modules, the requirement of hardware and software, data management and user-system interaction. 3.5 Architecture The proposed semantic based recommender system has client/server architecture as men-tioned in section 3.1, where users interact with the system through a web interface in which they search medical destination and update their semantic profiles. The platform of the system will be developed using technology of Java Plat-form, Enterprise Edition (J2EE), and will be supported with Jena Framework. The recom-

mender system in this research will be designed by usingfour main layers of related modules can be distinguished as described in Table 7. In or-der to develop recommender system, it is signif-icant to think about requirement of hardware where system will be deployed, and network (security), the medium used to transport the data. Table 8 describes the requirement of the recommender system from the view of hardware and network. 3.6 Data Management The Data management maintains all functionali-ty of data access layer, to manage the domain, user preference, user rating, transaction and log. The Data management consists of hierarchical information and the content of instance infor-mation. The hierarchical information of the class, property and instance is stored using OWL, and the content of instance information (physical data, e.g. hotel information, attraction data) is used a relational database. Figure 10 shows the data management process, include web crawler.

Figure 9. The link of the network built for spreading the activation level


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Table 7. A detailed schema of the system modules

Module Description The client-side a web-based graphical interface allow user to interact with the systems in real-time.

Interface will be developed using JSP helped by html5 and css3, which has been supporting semantic web.

The server-side Composed by those modules that receive requests from a client interface, and return the corresponding and recommendation responses. Jena framework will play important role to support this layer. Data communication between client and server side will be using https protocol.

The recommen-dation

Contains the proposed content-based strategy. Jena framework will help to develop.

The data man-agement

Provides functionalities to manage the domain, user preference, user feedback, transaction and log, exploited by the system using ontology (OWL and RDF) and relational database.

Table 8. Hardware and software need

Need Hardware Software Web Server Intel i7 3.8 GHz processor with 16 GB

DDRAM Windows Server 2008 R2 J2EE Jena 2 Framework

Database Server

Intel i7 3.8 GHz processor with 8 GB DDRAM.

Windows Server 2008 R2 Oracle 11G or MS SQL Server 2010

User’s device Fixed e.g. personal computer; mobile e.g. smart phone, tablet, and notebook.

Web-browser e.g. safari, firefox, andgoogle chrome.

Network LAN with 1 Gbps speed. http secure protocol (ssl)

MAPPING

Database Manager

Destination

Hotel Restaurant

Attraction Medical Provider

Flight

User

Profiling

User Profile Table

User IDUser NameUser Address...

Hotel Table

Hotel IDHotel NameHotel Location...

Table etc

...

Attraction Table

Attraction IDAttraction NameAttraction Table...

Service Manager

Ontology Repository

Web Crawler

Internet

Figure 10. Data management process


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For recommender system, data collection can be a substantial task, especially for the content of tourism resources, which involves a large num-ber of target web pages crawled. Beside collect content of tourism resource, the other use of web crawler is to collect user feedback of the content collected from others web page sources. In this proposed system, content of medical des-tination resources are automatically and periodi-cally retrieved from several online tourism ser-vices to record to the local repository using web crawler. 3.7 User-System Interaction A dynamic graphical interface accommodates the system to store the entire user’ inputs, ana-lyze their behavior and interaction with the sys-tem, update their preferences and adjust the rec-ommendation in real time. User feedbacks are taken into account via manual ratings and com-ments and user context can be gathered via au-tomatic process attached in interface. Figure 11 describes interaction flow between user and system. Users start access system by using fixed device (e.g. personal computer) or mobile de-vice (e.g. Tablet, Smartphone, etc.). Users should be registered in the system and enter their profile and medical condition, this is im-portant, because system need to acquire demo-graphic information of the user. Users input information about: name, address, phone, email, medical procedures, age, sex, passport, job, marital status, and insurance, then submit the registration form. After completing input their profile and medical condition, users start to search their medical destination by input their medical need. For example users choose dental as procedure type. Then, user input their duration time; this is for checking availability of the medical tourism resources. Or in the other way, users can limit the destination recommendation by using ad-vanced filter to select some criteria from medi-cal provider, transportation, attraction, and ac-commodation. Then system will retrieve all medical tourism resources available in preferred destination country and show the recommen-dation results to the user. Users have the flexi-bility to see the detail of information presented, like location of the medical provider, accommo-dation detail or attraction offered. If users need to change some criteria they can repeat the fil-tering process. If users already feel confident with the choices offered, they can quickly select the destination and continue to the next process.

After users choose their destination, system will generate confirmation of all information that users selected and detail information should be included, also system provides contact informa-tion of all medical tourism selected, e.g. contact information of medical provider or contact in-formation of accommodation. System also sends all information to users’ e-mail address.

Recommender System

User

Recommender System

User

z

No

Advanced Filter

Recommend Medical Destination Available

RS

Need to Filter Again?

Medical Procedure Need

Search

Start

User Personal Info

User Profiling

Time Availability

Advanced Filter ?

Select Medical Tourism/Traveling

All Tourism

Resource

Detail of Medical Destination Information

Send email to user and related.

Shows Confirmation of All

Information Process

Detail Information About Medical

Tourism/Traveling Selected by User

Finish

Figure 11. User-system interaction flow diagram

Medical Traveler

Recommender System

Detail Information

of Last Medical

Traveling

End

Retrieve and Show History of Traveling

Giving Feedback Search History of Traveling

User RatingStart

Saving User Feedback

List of History of Medical traveling

Figure 12. User feedback flow


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Users, who have had the experience of visiting the destination area, should fill in the user feed-back, the flow of users do feedback process can be shown in Figure 12, by giving ratings and comments to each medical tourism resources. By this process users can share their experiences and helping other users to make better choices and plan their traveling. 4. CONCLUSION and FUTURE RESEARCH

In this research, a design of recommender sys-tem for medical tourism has been proposed. The recommender will generate recommen-dation of all medical tourism resources, not only separat-ed for medical provider or accommodation or attraction, but all in one package provided to users. Recommender systems are designed with the technology of semantic web consisting of ontology and semantic association. Ontology is used to build a knowledge about personal user profile, as well as knowledge about the features of recommended items. The domain ontology produce user’s ontology of interest, which is explored by semantic association for choosing personalized suggestions to user. Semantic as-sociation performs as the content-based recom-mendation technique. The architecture of the recommender system is using client-server architecture which is very appropriate to combine with web-based applica-tions. The design has accomplished the person-alization main components: a database by using the data management layer, personal profiles and the knowledge about the features of the items that is recommended via domain ontology and the strategies for selecting personalized suggestions for each user with semantic associa-tion approach. Moreover, the design fulfills the three key fea-tures that should consist during designing the recommender system, using semantic associa-tion for recommendation technique, user profil-ing by using domain ontology and using client-server architecture and also based on the re-quirements of the system which is modeled with the environmental model. Finally, by using the ontology for domain knowledge, semantic association and capabili-ties of data management to gather information from other resources, the design of recommend-er system proposed in this research is trying to comply with the purposes of the semantic web for recording how the data relates to real world objects about common formats for integration and combination of data drawn from diverse

sources, where on the original Web mainly con-centrated on the interchange of documents. For the future research it is important to imple-ment the design into web based application, for measuring the acceptance of users, the accuracy of the recommendations offered and the perfor-mance of the system itself. The ability of the system can be improved by not only to provide suggestion on trip planning, but also provide suggestion when user has arrived in destination in case of change of plans. 5. REFERENCES

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