1/22 Exploiting a Map-based Interface in Conversational recommender Systems for Mobile travelers francesco ricci Free University of bolzano, Italy Quang Nhat Nguyen Hanoi University of Technology, vietnam Olga Averianova Image Data Systems Ltd, UK ABSTRACT nowadays travel and tourism Web sites store and offer a large volume of travel related information and services. Furthermore, this huge amount of information can be easily accessed using mobile devices, such as a phone with mobile Internet connection capability. However, this information can easily overwhelm a user because of the large number of information items to be shown and the limited screen size in the mobile device. Recommender systems(RSs)are often used in conjunction with Web tools to effectively help users in accessing this overwhelming amount of information. These recommender systems can support the user in making a decision even when specific knowledge necessary to autonomously evaluate the offerings is not available. Recommender systems cope with the information overload problem by providing a user with personalized recommendations (i.e, a well chosen selection of the items contained in the repository ) adapting this selection to the user's needs and preferences in a particular usage context In this chapter, the authors present a recommendation approach integrating a conversational preference acquisition technology based on"critiquing"with map visualization technologies to build a new map based conversational mobile Rs that can effectively and intuitively support travelers in finding their desired products and services. The results of the authors' real-user study show that integrating map-based visualization and critiquing-based interaction in mobile RSs improves the system's recommendation effectiveness and increases the user satisfaction Keywords: recommender systems, location-based services, conversational systems, travel planning INTRODUCTION The users of travel and tourism web sites often find it difficult in choosing their desired travel products and services due to an overwhelming number of options to consider, and the lack of the systems help in making product selection decisions. The problem becomes even harder for users of the mobile Internet who browse travel sites and their product repositories using a mobile device. This additional difficulty is
1/22 Exploiting a Map-based Interface in Conversational Recommender Systems for Mobile Travelers Francesco Ricci Free University of Bolzano, Italy Quang Nhat Nguyen Hanoi University of Technology, Vietnam Olga Averjanova Image Data Systems Ltd, UK ABSTRACT Nowadays travel and tourism Web sites store and offer a large volume of travel related information and services. Furthermore, this huge amount of information can be easily accessed using mobile devices, such as a phone with mobile Internet connection capability. However, this information can easily overwhelm a user because of the large number of information items to be shown and the limited screen size in the mobile device. Recommender systems (RSs) are often used in conjunction with Web tools to effectively help users in accessing this overwhelming amount of information. These recommender systems can support the user in making a decision even when specific knowledge necessary to autonomously evaluate the offerings is not available. Recommender systems cope with the information overload problem by providing a user with personalized recommendations (i.e., a well chosen selection of the items contained in the repository), adapting this selection to the user’s needs and preferences in a particular usage context. In this chapter, the authors present a recommendation approach integrating a conversational preference acquisition technology based on “critiquing” with map visualization technologies to build a new mapbased conversational mobile RS that can effectively and intuitively support travelers in finding their desired products and services. The results of the authors’ real-user study show that integrating map-based visualization and critiquing-based interaction in mobile RSs improves the system’s recommendation effectiveness, and increases the user satisfaction. Keywords: recommender systems, location-based services, conversational systems, travel planning INTRODUCTION The users of travel and tourism web sites often find it difficult in choosing their desired travel products and services due to an overwhelming number of options to consider, and the lack of the system’s help in making product selection decisions. The problem becomes even harder for users of the mobile Internet, who browse travel sites and their product repositories using a mobile device. This additional difficulty is
/22 due to the intrinsic obstacles of the mobile usage environment, i. e, mobile devices have small screens and the data transfer rates of wireless networks are typically lower than those of wired ones Recommender Systems RSs are decision support tools aimed at addressing the information overload problem, providing product and service recommendations personalized to the user's needs and preferences at a particular request context(Resnick Varian, 1997; Adomavicius Tuzhilin, 2005). However, existing recommendation technologies have not been developed specifically for mobile users; and this chapter shows that recommendation techniques developed for the wired web must be adapted to the mobile environment in order to better exploit the available information, and provide software tools usable on mobile devices The evolution of mobile devices(e.g, PDAs and mobile phones), wireless communication technologies (e.g, wireless LAN and UMTS), and position detection techniques(e.g, RFID beacon-based and GPS), ave created favorable conditions for the development and commercialization of a large number of location-based mobile services(Mohapatra Suma, 2005; Steinfield, 2004), i.e., information services accessible by mobile devices through the mobile network, and utilizing the geographical position of the mobile device. As a consequence, many location-based mobile services have been introduced in the recent years, including emergency services, information services, navigation support services, etc For example, mobile travelers can access local tourist information services providing information about Pospischil et al., 2002 as pubs and restaurants(Dunlop et al., 2004) get routing guidance from their position to a target location(Pospischil et al., 2002). In many of these systems, maps and map-based interfaces are used to visualize points of interests (e.g, restaurants, museums,or hotels), their spatial relations, and various kinds of information related to these points(e.g menus, opening hours, or in-room services) However, map-based interfaces do not solve all the information access problems. In fact, a major problem This ap-based visualization is the need to keep the display readable and free from irrelevant information This is particularly true in the mobile usage environment. Because of the limitations of mobile devices especially small screens and limited computing power, displaying on an electronic map a large number of objects and their related information is computationally expensive and usually not effective. Hence systems providing mobile travel services should employ filtering mechanisms to reduce the amount of data(information objects)that is displayed on an electronic map Though the specific benefits of map-based interfaces and recommendation technologies have been demonstrated in a number of previous research projects, the integration of these two technologies and their empirical evaluation, in terms of usability and effectiveness, in mobile travel support systems have not been studied yet. In this chapter, we present an approach for integrating recommendation technologies and electronic map visualization technologies to build a map-supported travel rs that can effectively and intuitively provide personalized recommendations on mobile devices Our recommendation methodology integrates long-term and session-specific user preferences, uses a composite query representation, employs a case-based model of the recommendation problem and its lution, furthermore, it exploits a critique-based conversational approach(Ricci& Nguyen, 2007). The integration of long-term and session-specific user preferences enables the exploitation of multiple knowledge sources of user's data. The long-term user preferences are derived (inferred) from past recommendation sessions, and on the other hand, the session-specific user preferences are collected through the users explicit input including the critiques made in the current session The composite query representation consists of a logical query and a favorite pattern. This allows using both strict logical constraints and weak similarity-based conditions. The logical query component helps
2/22 due to the intrinsic obstacles of the mobile usage environment, i.e., mobile devices have small screens and the data transfer rates of wireless networks are typically lower than those of wired ones. Recommender Systems RSs are decision support tools aimed at addressing the information overload problem, providing product and service recommendations personalized to the user's needs and preferences at a particular request context (Resnick & Varian, 1997; Adomavicius & Tuzhilin, 2005). However, existing recommendation technologies have not been developed specifically for mobile users; and this chapter shows that recommendation techniques developed for the wired web must be adapted to the mobile environment in order to better exploit the available information, and provide software tools usable on mobile devices. The evolution of mobile devices (e.g., PDAs and mobile phones), wireless communication technologies (e.g., wireless LAN and UMTS), and position detection techniques (e.g., RFID beacon-based and GPS), have created favorable conditions for the development and commercialization of a large number of location-based mobile services (Mohapatra & Suma, 2005; Steinfield, 2004), i.e., information services accessible by mobile devices through the mobile network, and utilizing the geographical position of the mobile device. As a consequence, many location-based mobile services have been introduced in the recent years, including emergency services, information services, navigation support services, etc. For example, mobile travelers can access local tourist information services providing information about nearby points of interests (Pospischil et al., 2002), such as pubs and restaurants (Dunlop et al., 2004), or get routing guidance from their position to a target location (Pospischil et al., 2002). In many of these systems, maps and map-based interfaces are used to visualize points of interests (e.g., restaurants, museums, or hotels), their spatial relations, and various kinds of information related to these points (e.g., menus, opening hours, or in-room services). However, map-based interfaces do not solve all the information access problems. In fact, a major problem in map-based visualization is the need to keep the display readable and free from irrelevant information. This is particularly true in the mobile usage environment. Because of the limitations of mobile devices, especially small screens and limited computing power, displaying on an electronic map a large number of objects and their related information is computationally expensive and usually not effective. Hence, systems providing mobile travel services should employ filtering mechanisms to reduce the amount of data (information objects) that is displayed on an electronic map. Though the specific benefits of map-based interfaces and recommendation technologies have been demonstrated in a number of previous research projects, the integration of these two technologies and their empirical evaluation, in terms of usability and effectiveness, in mobile travel support systems have not been studied yet. In this chapter, we present an approach for integrating recommendation technologies and electronic map visualization technologies to build a map-supported travel RS that can effectively and intuitively provide personalized recommendations on mobile devices. Our recommendation methodology integrates long-term and session-specific user preferences, uses a composite query representation, employs a case-based model of the recommendation problem and its solution, furthermore, it exploits a critique-based conversational approach (Ricci & Nguyen, 2007). The integration of long-term and session-specific user preferences enables the exploitation of multiple knowledge sources of user’s data. The long-term user preferences are derived (inferred) from past recommendation sessions, and on the other hand, the session-specific user preferences are collected through the user’s explicit input including the critiques made in the current session. The composite query representation consists of a logical query and a favorite pattern. This allows using both strict logical constraints and weak similarity-based conditions. The logical query component helps
3/22 the system to precisely focus on the most relevant subsets of the product space, whereas the favorite pattern component enables the system to correctly sort the relevant products, ranking higher the products that are most suitable for their needs and wants In our approach, a travel product recommendation session is modeled as a case, and the Case- Based Reasoning( CBR) problem solving strategy is used(Aamodt Plaza, 1994). CBR is based on learning from previous experiences, and case-based RSs exploit(reuse) the knowledge contained in a set of past recommendation cases. In our recommendation methodology, CBR is used for building a personalized user-query augmenting(by specializing) the original query derived from the conditions explicitly entered by the user. This personalized query is adapted in such a way that, taking into account the knowledge contained in the case base, the set of recommended products better match the users preferences, even if these preferences are not expressed in the original query entered by the user Furthermore, our recommendation approach is based on the interactive elicitation of the user's preferences through critiques. The user, instead of being required to formulate a precise search query at beginning of the interaction, is involved in a dialogue where the systems product suggestions interleave with the user's critiques to the recommended products. A user's critique is a comment Judgment)on a displayed product, which points out an unsatisfied preference(e.g, "I would like a cheaper restaurant)or confirms the importance of a product feature for the user("I'd like to have dinner in a restaurant with a garden terrace ). This critique-based user preferences elicitation procedure results in the system building a better understanding the users needs and preferences, and hence, in constantly improving the recommendations Our recommendation methodology has been firstly implemented in Moby Rek(ricci Nguyen, 2007),a previous empirical evaluations of Moby Rek, consisting of a live-user test(Ricci Nguyen, any o mobile Rs that supports travelers in finding their desired travel products(restaurants ) The results of som number of simulations(Nguyen ricci, 2007: 2008a), showed that our critique-based recommendation methodology is effective in supporting mobile travelers in product selection decisions. However Moby Rek employs a text-based interface for recommendation visualization and system-user interaction where the recommendations are presented to the user in a ranked list, as in a standard search engines like Google. We conjectured that this visualization and interaction approach may not be optimal for mobile devices and for mobile recommendation problems. We believed that this type of" standard" interface used for web applications can, in fact, cause some difficulties and inconveniences for mobile users in their interactions with the system Hence, in this chapter, we analyze the usability limitations of Moby Rek, which we believe are typically found in all list-styled RSs, and we illustrate how this analysis leads to the design of an extended and improved version of the system called MapMoby Rek. MapMoby Rek implements the same core recommendation techniques used in Moby Rek, i.e., the computation of the recommended items and their anking are done exactly as in Moby Rek. However, MapMoby Rek uses maps as the main user interface for information display and access; furthermore, it provides new decision-support functions based on the map. The design of the map-based interface of MapMoby Rek focused on the ability to offer the following user functions to enter the search query by specifying preferences for item features, to see the systems recommendations on the map ecognize immediately the differences between good and weak recommendations to compare two selected recommendations to input critiques to the recommended items to see on the map how the expressed critique influences the system s recommendations, and to select the best item(s)
3/22 the system to precisely focus on the most relevant subsets of the product space, whereas the favorite pattern component enables the system to correctly sort the relevant products, ranking higher the products that are most suitable for their needs and wants. In our approach, a travel product recommendation session is modeled as a case, and the Case-Based Reasoning (CBR) problem solving strategy is used (Aamodt & Plaza, 1994). CBR is based on learning from previous experiences, and case-based RSs exploit (reuse) the knowledge contained in a set of past recommendation cases. In our recommendation methodology, CBR is used for building a personalized user-query augmenting (by specializing) the original query derived from the conditions explicitly entered by the user. This personalized query is adapted in such a way that, taking into account the knowledge contained in the case base, the set of recommended products better match the user’s preferences, even if these preferences are not expressed in the original query entered by the user. Furthermore, our recommendation approach is based on the interactive elicitation of the user’s preferences through critiques. The user, instead of being required to formulate a precise search query at the beginning of the interaction, is involved in a dialogue where the system’s product suggestions interleave with the user’s critiques to the recommended products. A user’s critique is a comment (judgment) on a displayed product, which points out an unsatisfied preference (e.g., “I would like a cheaper restaurant”) or confirms the importance of a product feature for the user (“I’d like to have dinner in a restaurant with a garden terrace”). This critique-based user preferences elicitation procedure results in the system building a better understanding the user’s needs and preferences, and hence, in constantly improving the recommendations. Our recommendation methodology has been firstly implemented in MobyRek (Ricci & Nguyen, 2007), a mobile RS that supports travelers in finding their desired travel products (restaurants). The results of some previous empirical evaluations of MobyRek, consisting of a live-user test (Ricci & Nguyen, 2007) and a number of simulations (Nguyen & Ricci, 2007; 2008a), showed that our critique-based recommendation methodology is effective in supporting mobile travelers in product selection decisions. However, MobyRek employs a text-based interface for recommendation visualization and system-user interaction, where the recommendations are presented to the user in a ranked list, as in a standard search engines like Google. We conjectured that this visualization and interaction approach may not be optimal for mobile devices and for mobile recommendation problems. We believed that this type of “standard” interface used for web applications can, in fact, cause some difficulties and inconveniences for mobile users in their interactions with the system. Hence, in this chapter, we analyze the usability limitations of MobyRek, which we believe are typically found in all list-styled RSs, and we illustrate how this analysis leads to the design of an extended and improved version of the system called MapMobyRek. MapMobyRek implements the same core recommendation techniques used in MobyRek, i.e., the computation of the recommended items and their ranking are done exactly as in MobyRek. However, MapMobyRek uses maps as the main user interface for information display and access; furthermore, it provides new decision-support functions based on the map. The design of the map-based interface of MapMobyRek focused on the ability to offer the following user functions: • to enter the search query by specifying preferences for item features, • to see the system’s recommendations on the map, • to recognize immediately the differences between good and weak recommendations, • to compare two selected recommendations, • to input critiques to the recommended items, • to see on the map how the expressed critique influences the system’s recommendations, and • to select the best item(s)
4/22 Moby Rek and Map Moby Rek are then compared, through a real-user test, with respect to functionality efficiency, and convenience. The objective measures taken during the test and the subjective evaluations of the real-users show that the map-based interface is more effective than the list-based one. We also find that the integration of a map-based interface in a RS results in increased user satisfaction The remainder of this chapter is organized as follows. In the next section, we survey some related work Then, we present our recommendation methodology, we discuss the limitations of Moby Reks interface, and we present MapMoby Rek- the improved system. Next, we state our research hypoth describe the test procedure, and present and discuss the test results. Finally, we give our conclusions and indicate some avenues for future research. We note that this chapter extends(Averjanova et al., 2008)that more briefly describes Map Moby Rek and its validation RELATED WORK There have been several research works focusing on map-based mobile services(Meng et al., 2008), map- based mobile travel guides(Baus et al., 2005 ), recommender systems(Adomavicius tuzhilin, 2005 Burke, 2007), and travel RSs(Ricci, 2002; Fesenmaier et al., 2006). In this section, we first discuss traditional recommendation techniques, and then we focus on related works that aim to integrate mobile computing, map visualization and recommendation technologies in providing personalized travel products and services to tourists Recommendation Techniques Many recommendation methods have been introduced in the RSs literature. Nevertheless, they are often classified into four well-known categories: collaborative, content-based, knowledge-based, and hybrid (Adomavicius tuzhilin, 2005; Burke, 2007) Collaborative RSs generate recommendations using the information of the ratings given by users on items. In the collaborative recommendation approach, the system recommends to a given user those items that have been highly rated by the other users who have similar taste (i.e, similar ratings for co-rated items). The collaborative recommendation approach is inspired by the daily habit of people, i.e., when finding information or choosing between options, people often consult friends who have similar likes and Content-based RSs generate recommendations for a given user exploiting feature-based descriptions of items and the ratings that the user has given on some items. In the content-based recommendation approach, a user is modeled by a profile that represents the user's needs and preferences with respect to items features; and the system recommends to the user those items whose features(highly) match the users profile The knowledge-based recommendation approach uses specific domain knowledge to reason on the relationship (i.e, suitability/appropriateness) between a user's needs and preferences and a particular item In knowledge-based RSs, the system acquires the user's requirements on items(e.g, the user's query), and then consults the knowledge base to determine the best fitting items Each of the three recommendation approaches (i.e, collaborative, content-based, and knowledge-based) has its own limitations and disadvantages(Adomavicius Tuzhilin, 2005; Burke, 2007). Therefore, some RSs take a hybrid recommendation approach that combines two(or more)recommendation techniques i order to take full advantages of each individual technique, or to overcome some of their disadvantages
4/22 MobyRek and MapMobyRek are then compared, through a real-user test, with respect to functionality, efficiency, and convenience. The objective measures taken during the test and the subjective evaluations of the real-users show that the map-based interface is more effective than the list-based one. We also find that the integration of a map-based interface in a RS results in increased user satisfaction. The remainder of this chapter is organized as follows. In the next section, we survey some related work. Then, we present our recommendation methodology, we discuss the limitations of MobyRek’s user interface, and we present MapMobyRek – the improved system. Next, we state our research hypotheses, describe the test procedure, and present and discuss the test results. Finally, we give our conclusions and indicate some avenues for future research. We note that this chapter extends (Averjanova et al., 2008) that more briefly describes MapMobyRek and its validation. RELATED WORK There have been several research works focusing on map-based mobile services (Meng et al., 2008), mapbased mobile travel guides (Baus et al., 2005), recommender systems (Adomavicius & Tuzhilin, 2005; Burke, 2007), and travel RSs (Ricci, 2002; Fesenmaier et al., 2006). In this section, we first discuss traditional recommendation techniques, and then we focus on related works that aim to integrate mobile computing, map visualization and recommendation technologies in providing personalized travel products and services to tourists. Recommendation Techniques Many recommendation methods have been introduced in the RSs literature. Nevertheless, they are often classified into four well-known categories: collaborative, content-based, knowledge-based, and hybrid (Adomavicius & Tuzhilin, 2005; Burke, 2007). Collaborative RSs generate recommendations using the information of the ratings given by users on items. In the collaborative recommendation approach, the system recommends to a given user those items that have been highly rated by the other users who have similar taste (i.e., similar ratings for co-rated items). The collaborative recommendation approach is inspired by the daily habit of people, i.e., when finding information or choosing between options, people often consult friends who have similar likes and tastes. Content-based RSs generate recommendations for a given user exploiting feature-based descriptions of items and the ratings that the user has given on some items. In the content-based recommendation approach, a user is modeled by a profile that represents the user’s needs and preferences with respect to item’s features; and the system recommends to the user those items whose features (highly) match the user’s profile. The knowledge-based recommendation approach uses specific domain knowledge to reason on the relationship (i.e., suitability/appropriateness) between a user’s needs and preferences and a particular item. In knowledge-based RSs, the system acquires the user’s requirements on items (e.g., the user's query), and then consults the knowledge base to determine the best fitting items. Each of the three recommendation approaches (i.e., collaborative, content-based, and knowledge-based) has its own limitations and disadvantages (Adomavicius & Tuzhilin, 2005; Burke, 2007). Therefore, some RSs take a hybrid recommendation approach that combines two (or more) recommendation techniques in order to take full advantages of each individual technique, or to overcome some of their disadvantages
5/22 (Burke, 2007). With respect to this categorization of recommendation methods, our recommendation approach can be considered a knowledge-based approach Many RSs, such as those based on collaborative filtering, follow the single-shot recommendation strategy, i.e., for a given user's request the system computes and shows to the user the recommendation list, and the session ends. If the user is not satisfied she could enter a new query, if possible, but this process is up to the user and the system does not provide any support through this sequence of requests Conversely, in comversational RSs a recommendation session does not terminate immediately after the first set of recommendations are shown to the user, but it evolves in a dialogue where the system tries to elicit step-by-step the user's needs and preferences to produce better recommendations( Bridge et al 2005). In our approach, the system-user conversation is supported through critiquing, where the systems recommendations are interleaved with the users critiques to the recommended items (Burke, 2002 Mcginty smyth, 2006) Mobile Recommender Systems The systems and techniques described above have been mostly applied in the Web usage context, without any special concern with the context of the user, i.e., in particular, if she is on the go and accesses the travel and tourism support systems that have been specifically designed for the mobile usage conter ed information service using a mobile device. In the rest of this section we will review some personal City Guide(Dunlop et al., 2004)is designed for Palmos devices and helps tourists in finding attractions (such as restaurants) around a city. This recommender system uses the constraint-based filtering approach to control which attractions are shown on the map. In particular, the user, through the systems map interface, is asked to specify constraints on attraction type, restaurant cuisine and price. The system retrieves from the database only those attractions that satisfy the user's indicated constraints, and then ranks these retrieved attractions according to their match to the preferences stored in the users profile The system builds and updates the user's profile, which maintains her long-term preferences, by mining and interpreting the users actions (such as writing a restaurant review, reading a review, viewing a restaurants details, etc )and collecting the user's ratings to the restaurants. Though the system ranks the recommended attractions and shows them on the map, it does not visualize how close each recommended attraction is to the user's preferences (i.e., the recommendation level) Burigat et al. (2005)illustrate a system running on PDa devices that supports tourists in searching for travel products (i.e, hotels or restaurants) in a geographic area that best satisfy their needs and preferences. The system builds the user-query used to search in the services repository by asking the user to indicate her constraints on the service attributes, e.g. the facilities offered by the hotel or the restaurant However, the system does not employ the constraint-filtering approach or a multi-attribute utility function. Instead, the system constructs the recommendation list by ranking the services according to their satisfaction score. A services satisfaction score is measured by the number of constraints(indicated in the user's query) that are satisfied by the service. Hence, each recommended service is visualized by an icon superimposed on the map of the geographic area, augmented by a " filled-in"vertical bar representing how much the service satisfies the users query. We observe that this system does not reuse the knowledge derived from past user interactions to provide better recommendations Dynamic Tour Guide (dtg)(ten Hagen et al., 2005) is a mobile tour guide system that helps travelers in discovering a destination. Given a users request, the system computes a personalized tour in the city by asking the user her interests and the time she would like to spend for the visit. In DTG a tour is composed of a set of points of interest (i.e, restaurants, attractions and events). The system then presents the recommended tour on the map and provides some audio guide information. This audio visual presentation lets the user to visualize the tour itinerary. The system computes and presents just one tour per user
5/22 (Burke, 2007). With respect to this categorization of recommendation methods, our recommendation approach can be considered a knowledge-based approach. Many RSs, such as those based on collaborative filtering, follow the single-shot recommendation strategy, i.e., for a given user's request the system computes and shows to the user the recommendation list, and the session ends. If the user is not satisfied she could enter a new query, if possible, but this process is up to the user and the system does not provide any support through this sequence of requests. Conversely, in conversational RSs a recommendation session does not terminate immediately after the first set of recommendations are shown to the user, but it evolves in a dialogue where the system tries to elicit step-by-step the user’s needs and preferences to produce better recommendations (Bridge et al., 2005). In our approach, the system-user conversation is supported through critiquing, where the system’s recommendations are interleaved with the user’s critiques to the recommended items (Burke, 2002; McGinty & Smyth, 2006). Mobile Recommender Systems The systems and techniques described above have been mostly applied in the Web usage context, without any special concern with the context of the user, i.e., in particular, if she is on the go and accesses the information service using a mobile device. In the rest of this section we will review some personalized travel and tourism support systems that have been specifically designed for the mobile usage context. CityGuide (Dunlop et al., 2004) is designed for PalmOS devices and helps tourists in finding attractions (such as restaurants) around a city. This recommender system uses the constraint-based filtering approach to control which attractions are shown on the map. In particular, the user, through the system’s map interface, is asked to specify constraints on attraction type, restaurant cuisine and price. The system retrieves from the database only those attractions that satisfy the user’s indicated constraints, and then ranks these retrieved attractions according to their match to the preferences stored in the user’s profile. The system builds and updates the user’s profile, which maintains her long-term preferences, by mining and interpreting the user’s actions (such as writing a restaurant review, reading a review, viewing a restaurant’s details, etc.) and collecting the user’s ratings to the restaurants. Though the system ranks the recommended attractions and shows them on the map, it does not visualize how close each recommended attraction is to the user’s preferences (i.e., the recommendation level). Burigat et al. (2005) illustrate a system running on PDA devices that supports tourists in searching for travel products (i.e., hotels or restaurants) in a geographic area that best satisfy their needs and preferences. The system builds the user-query used to search in the services repository by asking the user to indicate her constraints on the service attributes, e.g. the facilities offered by the hotel or the restaurant. However, the system does not employ the constraint-filtering approach or a multi-attribute utility function. Instead, the system constructs the recommendation list by ranking the services according to their satisfaction score. A service’s satisfaction score is measured by the number of constraints (indicated in the user’s query) that are satisfied by the service. Hence, each recommended service is visualized by an icon superimposed on the map of the geographic area, augmented by a “filled-in” vertical bar representing how much the service satisfies the user’s query. We observe that this system does not reuse the knowledge derived from past user interactions to provide better recommendations. Dynamic Tour Guide (DTG) (ten Hagen et al., 2005) is a mobile tour guide system that helps travelers in discovering a destination. Given a user’s request, the system computes a personalized tour in the city by asking the user her interests and the time she would like to spend for the visit. In DTG a tour is composed of a set of points of interest (i.e., restaurants, attractions and events). The system then presents the recommended tour on the map and provides some audio guide information. This audio visual presentation lets the user to visualize the tour itinerary. The system computes and presents just one tour per user