《电子商务 E-business》阅读文献：Photo-Based User Profiling for Tourism Recommender Systems.pdf

Photo-Based User profiling for Tourism Recommender Systems Helmut Berger, Michaela Denk, Michael Dittenbach', Andreas Pesenhoferl, and Dieter Merkl2 E-Commerce Competence Center-EC3 Donau-City-StraBe 1, A-1220 Wien, Austria andreas. pesenhofer/ @ec3.at Institut fur Softwaretechnik und Interaktive Systeme, Technische Universitat wien FavoritenstraBe 9-11/188, A-1040 Wien, Austria dieter. merkl@ec. tuwien, ac, at Abstract. The World Wide Web has become an important source of in- formation for tourists planning their vacation. So, tourism recommender systems supporting users in their decision making process by suggesting suitable holiday destinations or travel packages based on user profi are an area of vivid research. Since a picture paints a thousand words we have conducted an online survey revealing significant dependencies between tourism-related photographs and tourist types. The results of the survey are packaged in a Web-based tourist profiling tool. It is now possible to generate a user profile in an enjoyable way by simply selecting photos without enduring lengthy form-based self assessments 1 Introduction Photographs bring moments back to life- be they very personal or moments shared by many. Assuming you are interested in relaxing and sunbathing in warm places with lots of sun, sand and ocean, you will have taken, without much doubt, pictures of sunny and sandy beaches. Conversely, if your primary emphasis is to remain active while on vacation, you may engage in your favorite sports and so take snapshots of your magic moments. All these moments, and thus tourism activities, can be categorized according to some typology of tourists and, in turn, it is possible to identify the relationship between these types and tourist activities 4. Grounded on this relationship we postulate the hypothesis that preferences for particular tourism-related photographs can be used to derive a tourist's type, and so, generate a profile of the user's likings. Currently, the process of creating such profiles can be a rather annoying, time-consuming and cumbersome task. however. intelligent services such as tourism recommender systems heavily rely on personal user profiles in addition to explicitly expressed needs and constraints. These systems focus on recommending destinations and product bundles tailored to the users'needs in order to support their decision

Photo-Based User Profiling for Tourism Recommender Systems Helmut Berger1, Michaela Denk1, Michael Dittenbach1, Andreas Pesenhofer1, and Dieter Merkl2 1 E-Commerce Competence Center–EC3, Donau-City-Straße 1, A–1220 Wien, Austria {helmut.berger,michaela.denk,michael.dittenbach, andreas.pesenhofer}@ec3.at 2 Institut f¨ur Softwaretechnik und Interaktive Systeme, Technische Universit¨at Wien, Favoritenstraße 9–11/188, A–1040 Wien, Austria dieter.merkl@ec.tuwien.ac.at Abstract. The World Wide Web has become an important source of information for tourists planning their vacation. So, tourism recommender systems supporting users in their decision making process by suggesting suitable holiday destinations or travel packages based on user profiles are an area of vivid research. Since a picture paints a thousand words we have conducted an online survey revealing significant dependencies between tourism-related photographs and tourist types. The results of the survey are packaged in a Web-based tourist profiling tool. It is now possible to generate a user profile in an enjoyable way by simply selecting photos without enduring lengthy form-based self assessments. 1 Introduction Photographs bring moments back to life – be they very personal or moments shared by many. Assuming you are interested in relaxing and sunbathing in warm places with lots of sun, sand and ocean, you will have taken, without much doubt, pictures of sunny and sandy beaches. Conversely, if your primary emphasis is to remain active while on vacation, you may engage in your favorite sports and so take snapshots of your magic moments. All these moments, and thus tourism activities, can be categorized according to some typology of tourists and, in turn, it is possible to identify the relationship between these types and tourist activities [4]. Grounded on this relationship we postulate the hypothesis that preferences for particular tourism-related photographs can be used to derive a tourist’s type, and so, generate a profile of the user’s likings. Currently, the process of creating such profiles can be a rather annoying, time-consuming and cumbersome task. However, intelligent services such as tourism recommender systems heavily rely on personal user profiles in addition to explicitly expressed needs and constraints. These systems focus on recommending destinations and product bundles tailored to the users’ needs in order to support their decision G. Psaila and R. Wagner (Eds.): EC-Web 2007, LNCS 4655, pp. 46–55, 2007. c Springer-Verlag Berlin Heidelberg 2007

Photo-Based User Profiling for Tourism Recommender Systems making process [1, 2, 8. QUite frequently, tourism recommender systems need to deal with first-time users, which implies that such systems lack purchase histories and face the cold-start problem 9. Some systems tackle this problem by request ing the user to answer a predefined set of questions. However, these might be misunderstood or simply remain unanswered 5. Such non-adaptive approaches are problematic, since poorly assembled user profiles reduce the quality of rec- ommendations, and consequently, negatively effect the acceptance and success of tourism recommender systems. a different line for user preference elicitation is taken in 7, where profiles for new users are generated based on Likert-scale ratings of products. The new user is required to assess her likings until suffi- cient overlap to profiles of known users can be derived. This is feasible in an application setting where commodities are being sold. In tourism, however, the constraints are different since the products are generally rather expensive and annual leave is limited In order to prove our hypothesis, we have conducted an online survey revealing gnificant dependencies between tourism-related photographs and tourist types Our results show that we can take advantage of this relationship and propose a profiling technique based on photograph selection, which minimizes the efforts for users formalizing their likings and get them as quickly as possible fun part. The results of the survey are further packaged in a Web-based tourist The remainder of this paper is organized as follows In Section 2 we present our online survey and some basic facts regarding the respondents Sections 3 and 4 contain our findings from the survey regarding motivating factors and significant photographs for various tourist types. In Section 5 we show the tourist type profiler developed based on the survey results. Finally, Section 6 gives some conclusions 2 The Online Survey To investigate whether tourist's preferences can be derived from tourism-related photographs we conducted a survey. An online questionnaire was made public in July 2006 on a Web portal. This questionnaire consisted of three parts whereof the first part aimed at obtaining personal and demographic data of the partici pants. These were age group, gender, marital status, number of children, highest level of education, and whether they live in a city or town The second part was designed to capture the tourism preferences of the par- ticipants. They were asked to select from a set of 17 tourist types based on the tourist typology proposed by Yiannakis and Gibson 10. The tourist types were described in terms of statements such as"interested in relaxing and sunbathing in warm places with lots of sun, sand and ocean"or"mostly interested in meet- ing the local people, trying the food and speaking the language"whereof the first description corresponds to the tourist type referred to as the Sun Lover and the latter to the Anthropologist. Note that we refrained from providing the actual la bels of the tourist types presuming that participants might be biased by these Additionally, we have defined four age groups, viz. less than 20, 21 to 40, 41 to 60

Photo-Based User Profiling for Tourism Recommender Systems 47 making process [1,2,8]. Quite frequently, tourism recommender systems need to deal with first-time users, which implies that such systems lack purchase histories and face the cold-start problem [9]. Some systems tackle this problem by requesting the user to answer a predefined set of questions. However, these might be misunderstood or simply remain unanswered [5]. Such non-adaptive approaches are problematic, since poorly assembled user profiles reduce the quality of recommendations, and consequently, negatively effect the acceptance and success of tourism recommender systems. A different line for user preference elicitation is taken in [7], where profiles for new users are generated based on Likert-scale ratings of products. The new user is required to assess her likings until suffi- cient overlap to profiles of known users can be derived. This is feasible in an application setting where commodities are being sold. In tourism, however, the constraints are different since the products are generally rather expensive and annual leave is limited. In order to prove our hypothesis, we have conducted an online survey revealing significant dependencies between tourism-related photographs and tourist types. Our results show that we can take advantage of this relationship and propose a profiling technique based on photograph selection, which minimizes the efforts for users formalizing their likings and get them as quickly as possible to the fun part. The results of the survey are further packaged in a Web-based tourist profiling tool. The remainder of this paper is organized as follows. In Section 2 we present our online survey and some basic facts regarding the respondents. Sections 3 and 4 contain our findings from the survey regarding motivating factors and significant photographsfor various tourist types. In Section 5 we show the tourist type profiler developed based on the survey results. Finally, Section 6 gives some conclusions. 2 The Online Survey To investigate whether tourist’s preferences can be derived from tourism-related photographs we conducted a survey. An online questionnaire was made public in July 2006 on a Web portal. This questionnaire consisted of three parts whereof the first part aimed at obtaining personal and demographic data of the participants. These were age group, gender, marital status, number of children, highest level of education, and whether they live in a city or town. The second part was designed to capture the tourism preferences of the participants. They were asked to select from a set of 17 tourist types based on the tourist typology proposed by Yiannakis and Gibson [10]. The tourist types were described in terms of statements such as “interested in relaxing and sunbathing in warm places with lots of sun, sand and ocean” or “mostly interested in meeting the local people, trying the food and speaking the language” whereof the first description corresponds to the tourist type referred to as the Sun Lover and the latter to the Anthropologist. Note that we refrained from providing the actual labels of the tourist types presuming that participants might be biased by these. Additionally, we have defined four age groups, viz. less than 20, 21 to 40, 41 to 60

and over 60. Each participant was asked to select those tourist types which she has belonged to in earlier periods of her life, or currently belongs to. For example, a participant aged 47 was requested to select her personal tourism habits when she was younger than 20, between 21 and 40 as well as her current preferenc The third part of the questionnaire comprised 60 photos depicting different ourism-related situations. Participants should identify those photos that best represent their personal tourism habits. In the end, we have gathered data from 426 respondents; their demographic composition in shown Table 1 Table 1. Personal and demographic characteristics of survey sample(n=426) g with long term partner-31l Resident of a ci The tourist typology is given in Table 2 and the descriptions as provided in the questionnaire are shown. Additionally, the absolute and relative frequencies of the respondents tourism preferences are given. Please note that the sum of the relative frequencies exceeds 100%, because most respondents obviously assigned themselves to more than one tourist type. The rank order of tourist types in this table significantly correlates(Pearson's r=0.895, a=0.001) with the results presented in 3 3 On pack and kick in tourism In order to generate a map of the relationships between tourist types and the photographs we carried out a correspondence analysis. Starting from a cross tabulation of photo click frequencies by tourist type, we obtained the correspon- dence analysis map depicted in Figure 1. The results show that the relationship between tourist type and photo can be mapped onto two dimensions that account for 56.44% of the inertia, i.e. a large amount of the total variance is explained by the first two principal axes. In particular, the x-axis can be referred to as the Pack Factor and the y-axis represents the Kick Factor. The Pack Factor identifies the level of collectivity"one can associate with a particular tourist type. Consider, for example, the explorer, which is the left-most tourist type, and the Organized Mass Tourist, the right-most tourist type along the x-axis The Explorer might be identified as a rather solitary individual compared to an Organized Mass Tourist, who is generally accompanied by a larger number of like-minded tourists. This interpretation is corroborated by the findings of a study in which tourist experiences have been identified to vary along an indi- vidualistic/collectivistic continuum [6. The Kick: Factor identifies the "level of excitement"one might associate with a particular tourist activity. The Thrill Seeker, for instance, is by definition interested in risky, exhilarating activities

48 H. Berger et al. and over 60. Each participant was asked to select those tourist types which she has belonged to in earlier periods of her life, or currently belongs to. For example, a participant aged 47 was requested to select her personal tourism habits when she was younger than 20, between 21 and 40 as well as her current preferences. The third part of the questionnaire comprised 60 photos depicting different tourism-related situations. Participants should identify those photos that best represent their personal tourism habits. In the end, we have gathered data from 426 respondents; their demographic composition in shown Table 1. Table 1. Personal and demographic characteristics of survey sample (n=426) Gender Female - 208; Male - 218 Age group 21 to 40 - 200; 41 to 60 - 187; 61 and above - 39 Education Primary - 148; Secondary - 156; University - 122 Marital status Single/separated - 115; married/living with long term partner - 311 Kids no kids - 189; one or more kids - 237 Resident of a city - 188; village/town - 238 The tourist typology is given in Table 2 and the descriptions as provided in the questionnaire are shown. Additionally, the absolute and relative frequencies of the respondents’ tourism preferences are given. Please note that the sum of the relative frequencies exceeds 100%, because most respondents obviously assigned themselves to more than one tourist type. The rank order of tourist types in this table significantly correlates (Pearson’s r = 0.895, α = 0.001) with the results presented in [3]. 3 On Pack and Kick in Tourism In order to generate a map of the relationships between tourist types and the photographs we carried out a correspondence analysis. Starting from a cross tabulation of photo click frequencies by tourist type, we obtained the correspondence analysis map depicted in Figure 1. The results show that the relationship between tourist type and photo can be mapped onto two dimensions that account for 56.44% of the inertia, i.e. a large amount of the total variance is explained by the first two principal axes. In particular, the x-axis can be referred to as the Pack Factor and the y-axis represents the Kick Factor. The Pack Factor identifies the “level of collectivity” one can associate with a particular tourist type. Consider, for example, the Explorer, which is the left-most tourist type, and the Organized Mass Tourist, the right-most tourist type along the x-axis. The Explorer might be identified as a rather solitary individual compared to an Organized Mass Tourist, who is generally accompanied by a larger number of like-minded tourists. This interpretation is corroborated by the findings of a study in which tourist experiences have been identified to vary along an individualistic/collectivistic continuum [6]. The Kick Factor identifies the “level of excitement” one might associate with a particular tourist activity. The Thrill Seeker, for instance, is by definition interested in risky, exhilarating activities

Photo-Based User Profiling for Tourism Recommender Systems 49 Table 2. Tourist types, their descriptions and distributions statistics Tourist type Description Freq. % Anthropologist Mostly interested in meeting the local people, trying the food and speaking the language 334 78.40 Escapist I Enjoys taking it easy away from the stresses and pressures of home environment 320 75.12 Archaeologist Primarily interested in archaeological sites and ruins; enjoys studying history of ancient civilizations 265 62.21 Sun Lover Interested in relaxing and sunbathing in warm places with lots of sun, sand and ocean 263 61.74 Independent Mass , Visits regular tourist attractions but avoids Tourist I, (IMT I) packaged vacations and organized tours 223 52.35 High Class Travels first class, stays in the best hotels, goes to shows and enjoys fine dining 207 48.59 Independent Mass Plans own destination and hotel reservations Tourist II, (IMT II) and often plays it by ear (spontaneous) 196 46.01 Escapist II Gets away from it all by escaping to peaceful, deserted or out of the way places 174 40.85 Organized Mass Tourist, Mostly interested in organized vacations, packaged tours, (OMT) taking pictures/buying lots of souvenirs 163 38.26 Active Sports Primary emphasis while on vacation is to remain active engaging in favorite sports 158 37.09 Seeker Seeker of spiritual and/or personal knowledge to better understand self and meaning of life 136 31.92 Explorer Prefers adventure travel, exploring out of the way places and enjoys challenge in getting there 132 30.99 Educational Tourist, Participates in planned study tours and seminars (Edu-Tourist) to acquire new skills and knowledge 127 29.81 Jet Setter Vacations in elite, world class resorts, goes to exclusive night clubs, and socializes with celebrities 104 24.41 Action Seeker Mostly interested in partying, going to night clubs and meeting people for uncomplicated romantic experiences 86 20.19 Thrill Seeker Interested in risky, exhilarating activities which provide emotional highs for the participant 61 14.32 Drifter Drifts from place to place living a hippie-style existence 55 12.91 that provide emotional highs. Contrary, the Escapist I enjoys taking it easy, away from the stresses and pressures of the home environment. The generated layout of photos is to a high degree in-line with the alignment of the tourist types. For example, photos 22 (alpine ski touring) and 37 (alpine skiing) are highly associated with Active Sports whereas photos 46 (whitewater rafting), 52 (sky diving), 56 (bungee jumping) and 59 (windsurfing) correspond to the Thrill Seeker. The Action Seeker is represented by photos such as 3, 21 and 29 all of which are party sujets. The photo layout also reflects the characteristics of the axes. For example, photo 27 shows the highest level of individualism; in fact it depicts a solitary hitch hiker. On the contrary, photo 14 represents a typical packaged tour enjoyed by a group of bus tourists. In terms of the Kick Factor, photos 1 (car rental area at airport) and 55 (group listening to tour guide) show a moderate level of excitement whereas photos 52 and 56 depict risky and exhilarating activities. Note that photo 30 (audience with an Indian Bhagwan) was selected by 11 respondents only and, thus, is regarded as a statistical outlier. The correspondence map is divided into four quadrants each of which reflecting peculiarities of a set of tourist types. The lower left quadrant, for example, describes a high level of individualism and rather tranquil activities. As a result, this quadrant contains tourist types such as the Anthropologist, Archaeologist as