A conceptual framework for personalized location-based Services (LBS) tourism mobile application leveraging semantic web to enhance tourism experience Fadhlun Mohamed Mahmood
Zailan Arabee Bin Abdul Salam
School of Postgraduate studies Asia Pacific University (A.P.U) Kuala Lumpur, Malaysia
[email protected]
School of Postgraduate studies Asia Pacific University (A.P.U) Kuala Lumpur, Malaysia
[email protected]
Abstract— Mobile location-based tourism applications provide guidance to the tourist on the move based on their preferences and context such as time and location. These applications depend heavily on the GPS which is used to determine the user location so as to deliver information that takes into account user preferences and context. However, the delivery of information that is relevant to the user in terms of their preferences and their contextual condition seem to be lacking in most of the applications. Semantic web technologies provide an opportunity to develop more intelligent location-based applications that would enable the delivery of information that is more accurate and relevant to the user. Thus, the objectives are; 1) to identify the benefits of leveraging semantic web technologies on the framework 2) to identify the techniques used to provide personalized activities recommendations based on the user preferences.3) to identify the methods used to build user profile that contains user preferences.4) to identify the key locationbased component that needs to be integrated in the framework. A conceptual framework for personalized mobile location-based tourism apps leveraging semantic web to enhance tourism experience is proposed. Keywords-component: Mobile location-based tourism, GPS, semantic web, personalization I.
INTRODUCTION
Location-based mobile applications are applications that leverage GPS technology embedded in devices such as Smartphone to determine the user current location. Such applications provide users with information such as friends nearby and point of interests [8],[35],[24] among these applications are applications that fall under tourism domain.
relevant to the user preference and contextual parameters such as time and location provides an added value which would enhance the user experience. Semantic web technologies will facilitate the development of more intelligent location-based services that would provide information that is relevant based on the user context and, even provide search queries that are more accurate [16]. The research context will focus on providing personalized recommendations to tourist’s who are in their respective destination by harnessing mobile location-based and semantic web technologies. Having identified the area of the study it enable the researchers to focus on the applicability of the framework in tourism industry and its contribution to the overall tourist experience and satisfaction. The aim of this paper is to present a generic conceptual framework for personalized mobile location-based service (LBS) tourism apps leveraging semantic web technologies to enhance tourism experience. Section 2 provide an overview of the framework and, highlights the benefit of using semantic web technologies in location-based service. Section 3 presents the details of the methods used to design the framework. Section 4 represent the conceptual framework scenario and, describe the activity recommendation filtering techniques used to provide personalized recommendations that takes into account user preferences and context. II.
[33] states that location-based tourism applications provide users with better tour experience since they will be able to access information anytime and anywhere. However, the accessibility of information at anywhere and anytime depends on the purpose of the application e.g. if the application will be used prior to visiting the destination; in a sense the application is only used for planning a trip or used when the tourist is at the destination. For the purpose of the research this framework is intended for tourists who are in their respective destination. In order to better improve the interactivity and further enhancement of the framework, providing filtered information
c 978-1-4673-4529-3/12/$31.00 2012 IEEE
SEMANTICS IN MOBILE TOURISM LOCATIONBASED SERVICES (LBS)
A. Overview of the framework The framework functionalities are conceptualized as follows; Presentation of information through the usage of mash-up information incorporated such as images for the locations, map to indicate nearby attractions. Personalization of information considers the user preferences (user profile), current user context such as time and
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location. Whereby, the user profile is developed through explicit and implicit method. For activity recommendations the framework considers a hybrid filtering techniques. These techniques include contextdependent content-filtering and context-dependent item-based collaborative filtering. Context-dependent content-filtering harnesses semantic analysis to filter items that match the user profile. Context-dependent item-based collaborative filtering considers similar items rated by other users to provide recommendations. Semantic based tourism knowledge-base is used to represent the tourism domain. Thus, the recommendation filtering techniques will use items available in the tourism knowledge-base to provide recommendations that takes into account user preferences and contextual parameters such as time and location B. Benefits of adopting semantic web technologies in location-based services (LBS) Semantic web is “an extension of the current web in which information is given a well-defined meaning, better enabling computers and people to work in cooperation” [17]. The main objective of semantic web is to transform the current web into a web of data [11]. Whereby data from multiple sources can be merged and mapped in order to allow interoperability among different applications. This can only be achieved by adding meaning on the web resources that would enable the machine to automatically infer relationships among different related concepts. Hence, enabling machines to understand the underlying meaning of different data and inferring the relationship among them enables the retrieval of meaningful and accurate results [6]. The benefits of semantic technologies in LBS are vivid such as through the application of ontologies and linked open data. The application of ontologies in areas such as tourism provides the means of automatically inferring relationships among different related concepts in a particular knowledgedomain. Accordingly ontologies provide location-based services applications with the capability of determining the user’s current context as well as his needs [28]. Linked open data initiative refers to a project that intends to bootstrap the web of data by publishing existing data sets using Resource description framework (RDF) thus, creating a number of links between them [14]. The process will involve publishing free open license data sets as RDF on the web and inserting data links among data items from different sources of data [7]. RDF is a semantic language that was developed to form a common way to describe information so it can be read and understood by machines [27]. Among the data sources published on the linked open data are GeoNames, DBpedia, US census data, YAGO and flickr to mention a few [7]. For example in [5] the developers used data sets about locations from linked open data such as DBpedia
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and interlinked those data with other data sets from GeoNames, YAGO and filckr wrappr to mention a few. III.
DESCRIPTION OF THE METHODS USED
There are numerous methods considered to develop the framework. In this section we describe the methods considered. A. Location-based services (LBS) Location-based services (LBS) refer to the delivery of data information tailored to the location and context of the mobile user [9]. Location-based services harnesses geographic information systems (GIS), internet, communication and multimedia technology in a mobile environment to deliver value added services to the user [22]. [13], [9] identify geographic information systems (GIS) as the heart of locationbased services. According to [34] GIS is the main tool for geographical information process, analysis, application and visualization since GIS is provide geographic information services to the LBS. For the purpose of this study GIS system is integrated within the framework. B. Semantic web technologies Ontology is defined as a data model that represents knowledge that can be facts, things and ideas as a set of concepts within a particular domain and the relationship between these concepts [10], [26], [18]. QALL-ME tourism knowledge domain is adopted to represent tourism domain for the framework. Linked open data refers to a project that intends to bootstrap the web of data by publishing existing data sets in RDF thus, creating a number of links between them [14]. Data sources for the adopted ontology are from DBpedia which contains data sets referring to locations, GeoNames contains data sets that refer to geolocations, YAGO contains data set that refers to museums and monuments and, flickr wrappr contains data sets regarding images that can be associated with the locations [5]. Knowledge-base comes into existence when instances are being populated within the ontology [18]. Thus, for the purpose of the framework tourism knowledgebased is considered. The tourism knowledge-base is formed using QALL-ME ontology with data sets from linked open data. C. Personalization Personalization is the process of providing or recommending products or services to the individual based on their personal preferences [31], [32]. The user profile contains information about the customer behavior, demographic and preferences [2], [31] .In order to accommodate different tourists’ preferences and maintaining an updated user profile a hybrid user profiling is considered. The hybrid user profile comprises of explicit and implicit user profiling [12]. Explicit is gathered from the user input to the system, where they might be presented with simple questions such as their interest, demographic data and duration of the trip [4], [30]. Implicit information is gathered from the
2013 3rd IEEE International Advance Computing Conference (IACC)
user previous behavior or interactivity with the system such as feedbacks and ratings[ 25],[19]. Therefore, the framework has considered the usage of hybrid user profile. Explicit user profile is gathered through a simple survey questions that requires the users to state their preferences explicitly. Implicit user profile is gathered through the user ratings of touristic attractions. D. Recommender filtering techniques Context-dependent recommendations [29] defines context as any information that is used to characterize the situation of an entity whereby, an entity can be referred to a place or person. Contextual information can refer to weather condition, location and time. For example in mobile tourist applications time and weather conditions can affect the kind of recommendation provided to the tourists. For example if it is night and cold we cannot suggest swimming as an activity to be carried out. Context-dependent recommendation provides recommendations based on contextual user preferences [1]. Context-dependent recommendations can take place in three ways; pre-filtering, post-filtering or contextual modeling [1]. These filtering processes can be incorporated with any traditional filtering techniques such as collaborative or content filtering. Content-filtering recommend items similar rated items from the past user experience or preferences [15]. Content-filtering utilizes the content in the data items to predict its’ relevancy against the user profile. The process of filtering begins by content analysis, learning the user profile and filter items that match the user profile.
appreciations based on their ratings for particular items [15]. Item-based provides recommendations based on item rates, this approach does not group users rather it takes item-to-item ratings [3], [23]. However, item-based collaborative filtering provides better quality results and, has better performance than user-based [6]. Hybrid filtering combines more than one filtering technique [29]. Most of hybrid filtering combines content and collaborative filtering. The purpose of using hybrid is to decrease errors and increase the accuracy of recommendation results. For the purpose of this framework we considered a hybrid filtering techniques that includes; contextdependent content filtering and context-dependent collaborative filtering. IV.
PROPOSED CONCEPTUAL FRAMEWORK
This section presents high level scenario of the framework to give a better idea about its conceptual mechanism. The framework is expected to provide personalized recommendation to tourists during trip-phase. The framework incorporates user profile, GIS integrated within the framework, tourism knowledge-base and recommender filtering techniques. Figure 1 illustrates the scenario. A tourist is at the bird park in Kuala Lumpur, where the location is denoted by P1. Based on their stated preferences the user is also interested in museums thus, system will display a number of nearby Museums denoted by M1. The process of providing recommendations considers the user preferences, current context such as the time of recommendation (day or night) and location.
There are number of techniques used to find items that would be matched against the user profile to provide recommendations e.g. keyword matching or vector space model (VSM) with term frequency-inverse document frequency (TF-IDF) and semantic analysis. Vector space model with (TF-IDF) this is a form of keyword matching whereby it applies feature weighting on the items that would have similarities with item features available in the user stated preferences or their previous ratings [21]. The semantic analysis approach considered in the content-filtering is based on item-profile approach. Whereby, the stated preferences are matched against the items available on the tourism knowledge-base. The main advantage of using semantics to filter items is that, it will consider the features in the items in relation to its semantic meaning to provide recommendations [21]. Collaborative filtering manipulates current user rating on an item with other users who have previously rated same or similar item [20], [3]. Collaborative filtering technique is further divided into two; user-based and item-based. User-based the system will predict the current user preferences by grouping users with similar
Fig. 1: Framework Scenario
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Therefore the GIS system will provide the route to the nearby point of interests specified on the user profile. Tourism knowledge-base contains a number of touristic concepts such as attractions including museums etc. The recommendation filtering techniques will infer the results to be displayed to the user by considering the following; user current location provided by the GPS and GIS system to provide the routes based on the tourist position, the system will extract the nearby tourist attractions from the tourism knowledge-base and, filter information based on the stated user preference stored in the user profile knowledge-base
A. Context-dependent content-filtering Figure 2 illustrates context-dependent content-filtering. This filtering process considers user preferences and context (time and location). Context-dependent pre-filtering technique is considered before matching the user preferences against the available items represent in the tourism knowledge-base. Hence, in our case context is referred to the time of recommendation (day or night) and the user location. Once the contextual parameters have been identified a traditional content-based filtering takes place by matching the user preferences against the available items. The method used by content-filtering is based on semantic analysis. For example the tourist is in Bird Park and, based on his explicit user profile he is also interested in museums. Hence, the recommender system will suggest the appropriate point of interests (POI) taking into account user profile and context such as time and location to provide recommendations.
Fig. 3: Context-dependent collaborative filtering
CONCLUSION The proposed framework highlights the technologies and techniques that can be considered in developing personalized tourism mobile location-based applications that leverages semantic web technologies. However, a considerable amount of work needs to be conducted to assess the feasibility of the techniques used in activity recommendation and, user profile. A development of a prototype would be beneficial to gain an insight on the technological acceptance of the framework at the implementation level. User privacy concerns need an indepth review as it will mark the acceptance of the framework with the general public. REFERENCES [1]
[2] Fig. 2: Context-dependent content-filtering
B. Context-dependent collaborative filtering Figure 3 illustrates context-dependent collaborative filtering. This filtering technique considers user profile and context such as time and location to provide recommendations. The filtering process considers user context thus, applying contextdependent pre-filtering technique followed by traditional collaborative filtering. The collaborative filtering considered is an item-based filtering technique. The main difference of this filtering technique with the latter is that, it considers ratings provided by the user on items related or similar to the one rated by the current user. Hence, the filtering technique will recommend similar or related item based on the rating provided by other users.
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