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A holistic approach to requirements elicitation for mobile tourist recommendation systems
Andreas Gregoriades1, Maria Pampaka2, Michael Georgiades3
1 Cyprus University of Technology Limassol, [email protected]
2 The University of Manchester, Manchester, UK [email protected]
3 Primetel PLC, Limassol, [email protected]
Abstract. Mobile recommendation systems (MRS) are becoming ever more popular in the tourism industry, due to their potential to declutter the decision-making process of tourists. Despite their proliferation, such systems seem to lack accuracy and relevance to the needs of their users. This paper describes the mobile recommendation problem and explores the relationships between personality, emotion, context and recommendations for tourists. Its aim is to investigate user-requirements of prospective mobile recommendation systems for tourists and the influence of personality and emotional state on user needs. To that end, a survey was conducted with tourists in Cyprus at a point of interest to identify their recommendation needs. Collected data have been analyzed and preliminary results indicate different user requirements among contextual factors. This indicated that the contextualization of these applications in accordance with users’ personality and emotional state is essential to realize their full potential.
Keywords: Mobile recommendation systems, user requirements, personality, emotion, context.
1 Introduction
Recommender systems (RS) gained acceptance as software tools and techniques for providing suggestions to users by utilizing the excessive information availability on the web. They use information retrieval and fusion techniques that provide personalized recommendations to users during decision-making and have been extensively used in the tourist domain. Their most important feature is their ability to predict users’ preferences and interests by analyzing their behavior and the behavior of other similar users. Commonly used recommendation techniques include collaborative filtering, content-based, knowledge-based and hybrid techniques [1,7,8,10].
An important aspect of RS that has not been adequately addressed in the existing literature is the relationship between recommendations and the combined user’s inherent characteristics such as their emotions, personality and physiology. Some of
these have been addressed in isolation and found significant towards improving recommendations [5,31,32]. Contextual information about location, temperature and weather were analyzed independently and also found to improve recommendation relevance and accuracy [15,41] through Mobile Recommendation Systems (MRS). This is a growing area of RS that utilize the ubiquitous and context-aware capabilities of smartphones. The availability of built-in sensors such as GPS, gyroscope, motion etc., enables MRSs to offer their users recommendations about things to do or buy on the go. Geo-recommendation is indeed redefining the way outdoor shopping is performed, bringing notable opportunities to the tourism industry. Existing MRSs in e-tourism acquire the user needs and desires, either explicitly through questions or implicitly by mining the user’s online activity, and suggest destinations to visit, points of interest, tour plans that include transportation, restaurants and accommodation, events/activities or even complete tourist packages. The main objective of tourist MRSs is to ease the information search process for the traveler and to convince the user of the suitability of the recommended services or product. MRSs, however, failed to gain wider acceptance primarily due to their inability to utilize effectively contextual information about their users such as time, location, emotion, activity, so as to make relevant and valuable recommendations on-the-go and/or, but also due to their difficulty of use [1]. According to the literature [1,13] the main issue in RS is the lack of understanding of the relevance of contextual information to the recommendation problem. This is due to a mismatch between user-needs and implemented MRS functionality that aims to support these needs and is a well-known issue in Design Science and Requirements Engineering domains. Additionally, MRSs mainly use a sterilized rationalistic approach using either collaborative filtering or content based filtering that focuses more on the recommendation itself, rather than how users’ decision making is inherently influenced by human factors, such as their personality, emotion or physiology. These constitute contextual information that need to be incorporated into the recommendation problem.
Designing and implementing RS requires a systematic process that initiates with the requirements elicitation phase. Generally, the development of successful software requires tremendous amount of time spent with the user being involved with prototyping, experimentation, and providing feedback in order for the developer to understand the problem domain and then to identify the requirements of the prospective system. The quality of the software improves when requirements are defined correctly at the early stage of system development, hence their importance is imperative. By definition, software requirements are a set of statements which the software system must implement, the qualities it must achieve, and the constraints it must satisfy. These requirements are defined at an early stage of system development and reflect user needs [2]. Requirements are classified into four categories: business requirements, user requirements functional and nonfunctional requirements. Business requirements describe the rationale for an organization implementing the system, while user requirements describe functionality of the system that users expect to have. In this paper we concentrate on the user requirements and on tourist users in particular. However, requirements engineering methodologies tend to focus on the software, rather than the people. While the last couple of decades have seen a move away from this, with advancements such as social modelling [3] and user stories,
which focus on why users want something in addition to what they want, the focus is still very much on the behavioral “why”. Work by Miller [4] introduced the concept of emotional goals which captures the desired feelings of stakeholders in a prospective system. This, however, serves as a means to an end that aims for the desired emotion after using the system. The challenge is to incorporate user emotion and personality as an influencing factor to the design of a system. Design of systems has now moved beyond its traditional goals of efficiency and ease of use, towards systems being designed for desirability, seductiveness, and persuasion, properties that are linked to inherent user attributes. This is more relevant in applications such as MRS where personality and emotion have an important effect on the acceptance of the recommendations [5]. The goal in MRS, is to accurately recognize the user’s context and accordingly tailor its response. Hence, there are two problems to be resolved: the recognition of user state and, the recommendation of best option given the identified state. The latter has been addressed by many authors [1,8,11,17,54], while the former is still an open problem due to the lack of reliable non-intrusive techniques and equipment for recognizing user physiological state and emotion. According to the literature [5], personality affects user needs and wants and the way users interact with products or services. Personality also influences the way people use software [6]. Therefore, it is imperative to incorporate user personality along with other contextual cues during the early stages of an application development process, such as the requirements elicitation phase.
Based on the above, the design of prospective MRS should adopt a holistic approach by incorporating all facets of the recommendation problem. This work partly addresses this goal by examining the disparity between MRS users’ needs and the implemented MRS functionality. It focuses on the investigation of the need to incorporate contextual information regarding users’ state (inherent and circumstantial), in the recommendation process. It reports on an exploratory study which identifies relationships between tourists’ information needs, decision making strategies, personality and emotional state with prospective MRS requirements. This aims to highlight inherent user requirements and the way they relate to personality and emotions. This is required to specify functionalities of future MRSs that will satisfy the contextual needs of their users.
The research questions addressed in this paper are the following. What design features (software requirements) do most of the tourists want to see in a future MRS? What is the relationship of personality with these prospective features? Is there an association between emotional state and type of recommendation?
The paper is organized as follows. The next section presents a review of the literature starting with RSs and their application in tourism and then focusing on their relationship with decision making, personality and emotion. This is followed by the research methodology, the data collection and the analysis before presenting the results. The paper concludes with a brief discussion and the main conclusions.
2 Literature review
2.1 Recommendation Systems
A RS is a computational system that can make meaningful recommendations to potential users and is currently one of the main application areas of machine learning and artificial intelligence in the information technology domain. Commercial application includes, online advertising and item recommendation [7] within Netflix, TripAdvisor, Amazon The last years have witnessed an explosive increase in the use of mobile technology among tourists [7, 8]. e-Tourism systems [9] provide a good opportunity for mobile services to assist travelers in their decision making by offering recommendations based on their preferences, current location, and weather (context).
There are different types of RSs depending on the technique used to make recommendations, including Collaborative filtering, Content-based, knowledge-based and Context-based. Collaborative filtering [10] is a popular method that bases its predictions on the behavior of similar users. The fundamental assumption behind this method is that other users’ opinions can be used to provide a reasonable prediction of the active users’ preference as similar users are expected to have similar preferences. Hence, if users agree about the quality or relevance of items, they are likely to agree about other items [11]. Content-based techniques on the other hand recommend articles/products/services that are similar to items previously preferred by a specific user [12, 54]. They are based on the analysis of descriptions (content) of items preferred by a particular user to determine the key attributes (preferences) that can be used to distinguish these items. These preferences are stored in a user profile. Similarity between user and item is performed by comparing each item's attributes with the user profile to identify what to recommend. In contrast with Content based approaches, Collaborative filtering does not require human intervention for tagging content because item knowledge is not required. Recommendations are made based on the nearest-neighborhood method, hence users whose rating profiles are most similar to that of the target user are considered relevant. Collaborative Filtering technique can be either user-based or item-based. In the former, a user receives recommendations for items liked by similar users, while in the latter a user receives recommendations of items similar to items he/she liked in the past. The drawback of this approach is the description of users as the average of their friends. These are called memory-based approaches since they are based on past data. Model-based approaches use a model of the user, created either explicitly or implicitly using experts or machine learning techniques and utilize user profile data. They can improve the performance of the recommender system by taking into account the preferences of the active user as well as the aggregate of the neighborhood users.
The Knowledge-based recommendation technique recommends items to users based on knowledge about the users, items and their relationships. Usually, such systems use a functional knowledge base describing how an item meets a specific user's need. This can be achieved based on relationships between user needs and a possible recommendation [13]. Case-based reasoning is a common technique used for
knowledge-based recommendation in which items are “cases” and recommendations are generated by retrieving the most similar cases to the user's query or profile [12]. Alternatively, pattern identification using association rules can be applied. If the above are not suitable, domain knowledge can be used to make these associations explicit in the knowledge-base. Knowledge based systems use explicit knowledge about the relationships among an item, user preferences, and recommendation criteria and are applied in situations where collaborative filtering and content-based filtering cannot be used due to data sparsity.
Traditional recommendation systems ignore key contextual information when making suggestions to users. The emerging context-based RS which notably involve spatiotemporal criteria in the recommendation algorithm utilize contextual data through location -based services. They are based on the use of location-awareness and “ubiquity”. The former refers to knowledge of the user’s physical position at a particular time, while “ubiquity” refers to the ability to deliver the information and services to users wherever they are, and whenever they need it. In the tourism domain contextual information is of key importance. Tourist MRS belong to this category and employ some Artificial Intelligence techniques to analyze the behavior of their users, learn their preferences and provide proactive recommendations depending on the context [14]. Other tourists MRS focus on suggesting attractions and use automated planners to schedule activities within temporal and geographic constraints [15]. They could provide opening and closing times of the attractions, or the time needed to go from one point of interest to another. This, however, is a very complex planning and scheduling problem that researchers try to solve using optimization techniques, such as ant colony or meta-heuristic iterative methods [16]. In the same vein, automatic clustering algorithms are used to classify tourists with similar preferences or similar features into groups [17]. Alternative techniques for the tourist recommendation include approximate reasoning methods, such as Bayesian networks. These are used to manage the uncertainty between the relationships of user preferences and available activities [18]. Rule-based systems are used to deduce user preferences from data or experts using machine learning [19].
The most frequently used recommendation techniques in the tourism domain are content-based and collaborative filtering. Content-based recommendation however is problematic because it only recommends items closely related to those the user liked in the past. This is referred to as the overspecialization problem. Hence, no new items are recommended [20]. Collaborative filtering on the other hand suffers from the new item problem, where new item cannot be easily recommended to other users because the new items have no ratings [20]. This is referred to as the data sparsity problem.
To address these problems, researchers have proposed knowledge-based RSs [13]. Rather than requiring a large amount of data (item rating from users), such methods require only sufficient knowledge to judge items similar to each other. For example, [21] developed a RS by using the knowledge of domain experts to describe the relations among items and features, and user-defined preferences of recommendations. However, the knowledge-based RS exhibited the cold-start problem and lack of explicit mechanism to identify the constructs used to describe
user preferences and item features. The cold start problem describes the case when the system needs to recommend an item to a new user with no past information regarding this user and the case when a new item has few or no ratings. Thus, the problem focuses on the sparsity and the lack of relevant information about users and items. Techniques to address this problem involve asking users to rate a number of items to gather information about their preferences. In the case of context-based RS, cold-start users represent users located in unfamiliar areas with no physical location histories. Social based collaborative filtering methods represent a solution to the cold start problem and use social neighborhood (users similar to active user) as an indication of active user preferences.
The tourism domain is notably a flourishing application field for MRSs, which leverages massive opportunities to provide highly accurate and effective recommendations for tourists based on personal preferences and contextual parameters. However, the recommendation accuracy of current MRS is far from being optimum. To improve this, it is essential to dive into the complexities of the tourist decision making process and their inherent properties such as personality and emotions, to identify how these influence decisions and how MRS can utilize relevant information to improve the accuracy of the recommendations.
2.2 Tourist decision making process
RSs promise to streamline the decision-making process of people, by decluttering the decision space. It is, thus, imperative to examine the theoretical background of decision making when designing artifacts that aim to support it. The main drawback of MRS is the danger of irrelevant recommendations or recommendations made at the wrong time. This will distract the user and will result in user rejecting the MRS. Hence to design effective MRS to serve user needs, it is essential to understand the decision-making process of tourists before and during their holidays, to identify which part of the process could be best served by MRS and the identification of the optimum recommendation time along with the best recommendation that will relate to the dynamic needs of its user.
Decision making research investigate the influence of internal and external factors of human decision maker. The most dominant decision-making models are the rational and the bounded rationality. Much work on tourist decision-making adopted the rational decision-maker model. In this paradigm tourists engage with the decision-making problem in a motivationally-driven process of searching for an efficient means of satisfying desires and needs in relation to travel [46]. This approach is based on consumer behaviour [47] knowledge that represents the rational decision-making process followed by consumers when deciding what service or product to buy. This is expressed as a directed search for information about available and accessible options to satisfy a desire and evaluation of these options against some criteria. In tourism research the dominant rationalistic approach to decision-making does provide some useful insights across tourism choice and could be applied during tourist destination choice stage, which occurs before the trip. However, it is less suited for the often
relatively unplanned, hedonic, opportunistic and impulsive decision-making that often characterises tourists’ behaviours on-site within a destination [47]. It is arguable that rational models of motivation and decision-making underestimate the importance of emotional processes in tourists’ behaviour [56]. There has also been criticism of the rational approach [57] highlighting insight from psychology that behaviour is an adaptive process based on interaction.
The Bounded rationality model [22] offers a more realistic view, claiming that, time constraints, cognitive capacity and incomplete information, make individuals decide on a ‘good enough’ (‘satisfying’) solution rather than the optimal [22]. Related to this, [23] claimed that decisions are made only where an alternative is definitively better than the status quo and expresses this with the rationality of tourists bounded by constraints including travel stimuli, psycho-social state and environmental conditions.
With regard to the temporal aspects of tourist decision making, range from planned and early decisions before trip, to more spontaneous decisions at a destination [51]. Gunn [52] classifies decision regarding a trip into: primary (before trip), secondary (list of ‘to do’s’ at a destination) and tertiary (dynamically encountered at a destination). RSs can support all three levels of decision making, while MRSs focus more on supporting secondary and tertiary decisions relating while at a destination.
Generally, tourist decision making models are informed by models of consumer behaviour [24]. The main variables in these models relate to socio-psychological processes, personality and environmental variables. Alternatively, formal approaches to modelling tourist behaviour are based on economic and marketing theories [49]. Such modelling, however, operates on reasonably coarse-grained assumptions about the relevant properties of the tourist and the environment within which tourists express their behaviours. Finer-grained approaches to modelling tourist behaviour have a bottom up approach and focus on individual tourists’ presumed decision-making strategies. They model tourists’ behaviour using [50] the agent-based paradigm in which tourist decision makers are represented by agents that interact in a simulation environment.
Based on the above it is evident that tourist decision making process is a subset of tourist behavior which in turn is a subset of consumer behavior in which emotions and personality are highly influential. Therefore, modelling these effectively in MRS seems to be imperative to address current limitations of MRS and effectively support spontaneous tourists’ decisions, by utilizing inputs from diverse sources external and internal to the user. However, due to the difficulty of eliciting inherent emotional and physiological properties of tourists on the fly, it is essential to investigate approaches of eliciting this knowledge. This will make the link between user requirements of prospective MRS and personality, emotion and context more explicit.
2.3 Personality in recommendation
Most approaches for building RSs focus on recommendation precision and they ignore how users are inherently influenced by their own emotions and personality, and other human factors, during decision making. Personality is one of the factors that differentiate individuals and has been found to affect the way users interact with technology [6]. By definition, personality is referred to the set of emotional, attitudinal and interpersonal processes that are specific to each individual person [25]. Consequently, personality can be considered as one of the most important factors influencing human behaviour as it can affect how people react, behave and interact with other individuals. There is evidence relating personality of individuals and their tastes and interests, for example, affective experience and social behaviour [26]. This implies that individuals with matching personalities might have similar interests, which has direct application in the tourism domain.
Several studies aimed at finding the best features to describe someone’s personality. Tupes and Christal [28] were the first authors to identify five features in personality, while a model composed of five features known as the Big Five model was presented in [29]. This model is recognized as a valid mechanism for defining the most essential aspects of personality as expressed in the following 5 dimensions: Agreeableness, Extraversion, Openness to Experience, Conscientiousness and Neuroticism. Additional work [30] presents the development and validation of the 10-Items Personality Inventory Questionnaire that is a short version of the big five model (also used in the current study, see Methodology section).
With regards to software systems requirements, personality has also been found to be significant in predicting the required functionality of software systems [27]. Specifically, extraverts and emotionally stable people demonstrate higher intention to use new technology, while ‘open to experience’ people show a positive relationship with system’s ease of use. Extraverts are more likely to use technology, prefer to use applications on their mobile phone, and are also more likely to act based on the opinions of those whom they consider as significant [27]. This is the underlying theory used in collaborative filtering technique of RSs.
Despite the importance of personality in the recommendation problem, the literature on application of personality in tourism recommenders has been scarce. There are however some applications of personality in movie recommenders, such as in [5] where the authors added personality scores to a content-based movie RS to generate more personalized recommendations. Their comparative analysis revealed that the results from the recommendation were superior when personality was taken into consideration. In the same vein, in [31] and [32] the researchers included personality scores using the Big Five Model, as complementary information in traditional rating-based collaborative filtering RS. Their approach was compared to a traditional rating-based filtering system, showing that the system combining ratings and personality significantly outperformed the systems solely using either ratings or personality features alone. Consideration of personality traits in the tourism domain include work by [33, 34] that employed personality along with places of interest to make tourist
recommendations. These results indicate that personality could significantly improve MRS recommendation in the tourist domain.
2.4 Emotion, physiology and recommendation
Emotions are directly linked to decision making and personality. Emotions are considered to be critical to understanding the underlying reasoning of consumers' behaviors in marketing literature [35] as they can explain variations in individuals' responses beyond rationality [55]. According to [36], there are six main emotions: happiness, sadness, fear, surprise, anger, and disgust. They are the dominant driver of most significant decisions in life and can also affect human behavior. Therefore the need to incorporate emotions in future MRS seems promising.
Essentially, decisions guide people’s actions towards avoiding negative and increasing positive feelings [53]. Emotions emerge after we evaluate and interpret an event or stimulus [37]. They emerge from the subjective evaluations of a situation or an event. However, the same event can provoke different emotions for different individuals depending on how the event influences them.
Therefore, to improve MRS recommendations, it is useful to know the users’ emotional state (positive/negative) at any given time [38]. Some studies have also shown that emotions are influenced by time and surroundings [39] which is also an important aspect when designing an MRS, to identify the optimum time to make the recommendation to its user. This need becomes more apparent when travelling [40]. Nawijn [41] also reported that temperature may lead to the change of travelers’ emotions. This is particularly important for MRS that should not distract or annoy the user. Morris and Geason [42] showed that emotions can explain personal intentions more than cognition, while [43] highlights that emotional changes are fundamental determinants of tourists' decisions and account for 45% of the variance in tourists’ intentions to visit a place.
In the same vein, the impact of user’s physiological states such as level of fatigue and mood have been mostly ignored in the literature. When fatigued, users loose interest on most activities. With regards to MRS, user physiology is highly relevant. According to [44] users could become annoyed and loose trust in the system if the same item has been presented multiple times. In their work they utilize user mood (fatigue, tiredness) to recommend tourists attractions. Similarly [45] also recognized the importance of user physiology to the recommendation problem. Their MRS SenSay adjusts its recommendations dynamically to changing environmental and physiological shapes of its user. SenSay utilizes mobile sensors such as accelerometers and light sensor in accordance with physiological sensors mounted at numerous points on the body to provide data about the users’ physiology.
Based on the above, it is important to highlight that an isolated analysis of the recommendation problem using a subset of the total dimensions of the recommendation problem such as preferences, content, users, or context alone yield in
mediocre results. Research indicates that the solution lies in holistic approaches that integrate all aspects of the recommendation problem. This is the method employed in this work.
3 MethodologyThe main limitation of existing approaches to MRS stems from the fact that they do not take a holistic view at the problem but, instead, concentrate only on a few dimensions such as users’ past preferences and geolocation. This, in essence, yields mediocre results. To improve user recommendation, it is important to employ a holistic approach that integrates not only external cues (such as environmental, historical and contextual data), but also internal information, relating to the psychological and physiological state of the user. The method proposed herein is based on this paradigm and as such falls under the category of hybrid MRSs. However, due to the wide scope of the problem space, the focus of the research will be on the elicitation of the user requirements of MRS using field-based questionnaires to address each dimension of the problem (psychological, physiological, contextual etc.).
Another limitation of existing MRS frameworks is the scarcity or unavailability of data for the training of the recommendation algorithm, which is also referred to as the cold-start problem [8]. Most MRS use secondary data from social networks to hypothesize user behavior and to develop recommendation models. These, however, are based on mere assumptions of the tourist experience. Even though in some cases this approach yields good results, in our case, historical data regarding the emotional and physiological state of users is not available, nor collated data regarding all the dimensions of the external influences such as weather, traffic conditions and user’s social networks activity. To tackle this limitation, primary data (internal and external) are obtained directly from tourists while on their holidays in Cyprus. This initial knowledge is pre-processed and subsequently utilized to identify the user needs with regards to new MRS requirements.
Fig. 1: Hypothesized Conceptual model of features influencing MRS requirements
MRS requirements
Context
Emotional State
Personality
Physiology
User needs
The proposed conceptual model is depicted in Figure 1. As shown, tourists’ personality, emotion, physiology and context are proposed to relate to different features of a prospective MRS. According to the literature, there appears to be links between physiology, context, personality and emotions, and these properties then influence user-needs that are expressed in terms of user requirements.
The methodological approach of this study is composed of the following steps. Initially a literature review was conducted in the fields of personality, emotion, mood, and tourist decision making to identify the main constructs for the design of the instrument for data collection. Subsequently a pilot study was conducted to verify the quality of the questions used in the questionnaire. The second task involved identifying candidate tourists that could take part in the survey research. The first criterion for participant selection was the location of the participant and their activity status. Therefore we recruited only foreign tourists during their holiday activities. This was necessary in order to identify among others, MRS requirements that relate to user needs during their daily activities. The data collection was conducted at different tourist attractions in the Limassol area with many points of interests, restaurants, cafeterias, shops, museums and tourist activities at each point. Participants were interviewed while they were approaching the attraction to distill strategies they used when deciding their spontaneous activities or when selecting a point of interest to visit. The research instrument used to acquire user personality is based on the simplified Big five model as was adapted by [30], namely the Ten-Items Personality Inventory Questionnaire. This instrument is used in this study due to its validity and simplicity. Emotional and physiological state questions were used to capture users’ mental and physiological state using the main emotions [36] and main physiological states as reviewed earlier [44]. Contextual information included the current state of the user (active, relaxing etc), location, weather and time. During interaction with participants, the following information was also collected: purpose of their visit to Cyprus along with demographics, their interests, food preferences, recommendations they would prefer based on their current state from a hypothetical MRS.
Based on tourists’ current situation (geolocation, emotional & physiological state, preferences and personality type), participants were asked to choose a recommendation from a list of candidate predefined options. Participants were also asked to denote the best type of recommendation(s) given their current situation irrespective of the list provided in the point above. The above data was integrated and annotated with spatiotemporal and weather information. This aimed to identify links between their current state and required service.
Collected data was preprocessed and subsequently analyzed to identify relationships among different constructs. Data-mining and statistical modelling techniques will be used to identify and investigate patterns in the dataset. The decision making strategies and patterns that emerged from the data analysis will be used as the basis for the specification of the MRS functionality and subsequently its implementation in future work.
4 Analysis and results
The designed questionnaire was in English and all participants were fluent in the English language. This study was based on a sample of seventy participants: 27 males (39%) and 43 females (61%). Twenty-six were British (38%), twenty-two from other European country EU (36%) and sixteen non-EU nationals (26%). Forty-nine (69%) of them had higher education qualifications (College 12, Bachelors 20, Masters 15, PhD 2). Their mean age was 41.1, with 44 (66%) being employed, 14 (20%) retired, 5 students (7.1%), and 3 (4,3%) not employed. The majority were in Cyprus with their spouse or family (76%), 20% with friends or colleagues, and 4% alone. The resulting sample reported a variety of preferences with regards to their purpose of visit to Cyprus: beach holiday (35%), walking and nature (26%), cultural holiday (23%), nightlife and clubbing (11.4%), relaxation (55%), active holiday (13%).
Initially we conducted a nonparametric Spearman’s correlation analysis which revealed some following links between personality traits and MRS user-needs based on contextual information of user’s situation (Table 1). The relationships are color-coded based on positive (blue) and negative (red).
Table 1. Correlations between personality and MRS user-needs - On the Go
On the go MRS… Enthusiastic Argumentative Reliable Anxious
Open To
Experiences Quiet Caring Disorganized Calm
Conserva
tive
Events -.103 .199 .154 .065 -.060 .033 .096 -.013 .141 .019
Sport Activities .212 .119 -.023 .069 .006 .009 .034 -.116 .132 -.019
Attractions -.105 .187 .112 .036 -.337** -.129 .073 -.134 -.091 .410** Restaurants -.117 .200 .222(*) -.024 .008 -.045 .126 -.090 .066 .044
Cafeteries -.097 .273* -.105 -.024 -.284* .009 .013 -.096 -.075 .002
Transportation Methods -.213(*) .205 -.023 .140 -.326** .154 -.219(*) .051 .020 .101
Offers Near Me .112 .044 .170 .075 -.201 -.064 .290* -.067 .105 .174
Physical Effort .002 .156 -.039 .123 -.239(*) -.011 .003 .023 .020 .122
Other People Preferences -.092 -.082 -.009 .069 .017 -.067 .106 -.022 -.005 -.030
Very Close Friends -.073 .007 .050 .148 -.014 -.073 .103 .019 -.048 -.039
Activities Near Me .068 .081 .048 .031 -.122 .122 .094 -.139 .084 -.047
Tiredness Recommend .039 .190 -.220(*) -.124 -.178 -.045 .108 -.078 .044 -.035
Hungry Recomend -.103 .180 -.050 -.084 -.169 -.092 .111 .045 .015 .104
People With Similar
Preferences -.102 .101 -.129 .095 .088 -.028 -.006 .104 -.236(*) -.083
N=61 to 65 , (*) p<0.1, *p<0.05, ** p<0.01
As can be seen, anxiety, quietness and disorganization were not found to relate significantly with any on the MRS user needs. From the statistically significant relationships we can derive the following: Enthusiasm is negatively correlated with transportation methods meaning that the more enthusiastic the responded reported the less they need information on transportation. More argumentative respondents report more need for information on coffee places, whilst more reliable for restaurants. Reliability is also negatively related with the need for MRS to recommend information on tiredness. As shown with the negative relationships of ‘open to experiences’ personality, the more open respondents tend to need less information on attractions, coffee places, transportation methods and physical effort required. Conservative respondents in contrast tend to report more need for information on attractions. In other analysis (not shown here) enthusiastic trait was more related to beach holiday and less with eco-tourism, while argumentative tourists were less likely to be on spiritual tourism. Conservativeness was positively correlated with spiritual tourism and walk/Nature. Disorganization was positively correlated with Relaxation activities and positively related with last minute decisions such as use of UBER
service. Conservativeness was found negatively related with the use of social networks while on holidays. Anxiety was positively related with consulting travel agents when making decisions regarding where to go while at a destination and enthusiasm trait showed a negative link to using tourist information centers for decision support while on holiday.
With regards to the limitations of existing MRS capabilities, argumentative trait was positively related with limited recommendations from available MRS, indicating that more argumentative users required more variety of recommendation from future MRS. Enthusiastic trait showed positive relationship with current MRS being difficult to use, indicating that the usability of current MRS is not satisfactory.
Additionally, a nonparametric correlation analysis between tourists’ emotional state and need for MRS recommendations was performed separately for each personality trait. To that end each personality trait data was split into different sets and for each set a correlation analysis was performed. This analysis revealed the following patterns. Quiet and caring traits when reporting that they are feeling “calm” or had “full stomach” were found to be linked positively with the need for recommendation for “coffee shops” near-by. The same personality traits when where experiencing “arousal” or “excitement” were positively correlated with the need for recommendations for “sport activities”. Open and caring traits when experiencing “happy” feelings were found positively correlated with “Bars” recommendation. These initial patterns indicate preliminary Rules (either deterministic or stochastic) that can be utilizes by the prospective MRS to make recommendations to tourists when the preconditions of the rules are med. Subsequent analysis using data mining techniques reported in section 4.1 illustrates the level of support and confidence of each rule that is identified.
Table 2: Correlations between personality and evaluations of prospective MRS features. Enthusiast
ic Argument
ative Reliable Anxious Open To
Experiences Quiet Caring Disorgani
zed Calm Conserva
tive Trust -.159 .005 .257* .026 -.024 .340** .023 .098 .053 -.014 Trust After Tasting -.197 -.064 -.092 .035 -.222 (*) -.017 .077 -.007 .039 -.115 Intent To Use It -.037 .116 .075 -.082 -.171 .036 -.022 -.060 .000 -.074 Improve My Experience -.145 .097 .165 -.057 -.035 .069 .042 .208 .107 -.079 Save Me Time -.107 .204 .247(*) -.016 .128 .036 -.164 .236(*) -.027 -.064 Prevent Me From Pitfalls -.035 .224(*) .189 .153 -.001 .268* -.307* .226(*) -.123 -.036 Save Me Money -.055 .081 .143 .073 -.038 .395** -.010 -.063 -.011 -.115 Reduce The Hassle Of Info -.276* .202 .289* .085 .052 .116 -.112 .303* -.045 -.069 Reduce Uncertainties And Stress
-.009 .197 .268* .105 .222(*) .214(*) -.207 .255* -.060 -.097
Most Likely Purchase Such An MRS
-.063 .132 .052 .056 -.167 .174 -.079 .147 .062 -.128
MRS used By My Friends -.203 .036 -.103 .265* -.012 .365** -.305* .132 -.159 .075 Notes: N=58 to 62, (*) p<0.1, *p<0.05, ** p<0.001
Participants were also asked to evaluate the MRS technology in general using a 5point Likert scale, and these responses were analysed first in relation to the personality trait of the respondent. The results in Table 2 show the nonparametric correlations between these evaluations and respondents’ reported personality traits. Significant
relationships are highlighted on the table and colour coded for positive (blue) and negative (red). As can be seen, for example, the more reliable the respondent reported, the more likely they are to evaluate higher the MRS in relation to trust, saving time, reducing information hustle and reducing anxiety and stress. The latter feature of MSR is actually positively related with other personality traits as well: quietness, open to experience and disorganization. The more disorganized the responded the more positively they also evaluate MRS in respect to reducing information hustle, saving time and preventing them from pitfalls.
4.1 Patterns Identification
To identify patterns among personality, emotional state, physiology, user preferences and user needs, an association rules technique was used. The association rules algorithm used is the Apriori algorithm [58] since it is considered mainstream. The Apriori algorithm uses the support and confidence measures to generate valid association rules. Support is the percentage of instances of records in the dataset for which a pattern (rule) is true. For example, the support for the association rule A->B is the total number of instances containing both A and B divided by the number of total instances of the dataset. Confidence is the level of certainty that describes each discover pattern. For instance, the confidence for the rule A->B is the number of instances containing both A and B divided by the number of instances containing A.
The Weka tool was used for this purpose. The data underwent preprocessing to reduce its dimensionality. Essentially since personality traits were expressed by ordinal variables, it was necessary to convert them into binary variables using a threshold value of 3 and above for positive existence (denoted the value of 1) of the trait and below 3 for negative (denoted the value of 0). The rules that emerged also highlighted the importance of personality traits on user requirements. The selection of the rules was based on a confidence and support threshold. Figure 2 illustrates the main rules that emerged from the analysis. Indicatively the second rule states that if the personality trait is “anxious” then participants also like relaxation holidays. This rule has confidence 89% and above.
cultural=2 29 ==> conservative=1 26 <conf:(0.9)> lift:(1.26) lev:(0.08) [5] conv:(2.07)
anxious=1 38 ==> relaxation=1 34 <conf:(0.89)> lift:(1.12) lev:(0.05) [3] conv:(1.52)
anxious=1 shops=2 25 ==> relaxation=1 24 <conf:(0.96)> lift:(1.2) lev:(0.06) [3] conv:(2.5)
anxious=1 sightseeing=1 28 ==> relaxation=1 25 <conf:(0.89)> lift:(1.12) lev:(0.04) [2] conv:(1.4)
festival=2 shops=2 27 ==> sightseeing=1 24 <conf:(0.89)> lift:(1.15) lev:(0.05) [3] conv:(1.54)
argumentative=1 anxious=1 30 ==> relaxation=1 26 <conf:(0.87)> lift:(1.08) lev:(0.03) [2] conv:(1.2)
conservative=1 festival=2 29 ==> spirit=2 25 <conf:(0.86)> lift:(1.26) lev:(0.07) [5] conv:(1.82)
ecotour=2 relaxation=1 29 ==> conservative=1 25 <conf:(0.86)> lift:(1.21) lev:(0.06) [4] conv:(1.66)
conservative=1 50 ==> relaxation=1 42 <conf:(0.84)> lift:(1.05) lev:(0.03) [1] conv:(1.11)
argumentative=1 festival=2 30 ==> sightseeing=1 25 <conf:(0.83)> lift:(1.08) lev:(0.03) [1] conv:(1.14)
conservative=1 ecotour=2 30 ==> spirit=2 25 <conf:(0.83)> lift:(1.22) lev:(0.06) [4] conv:(1.57)
Fig 2. Emerged Association rules using Weka tool
5 Conclusions and future directionsPersonality is one of the most commonly used differentiators of individuals and has been found to affect tourist decisions and information needs in prospective MRS. In the same vein, emotion and physiology are additional dimensions that affects decision making. Emotion influence information needs and decisions and is directly linked to user needs of prospective MRS plus the propensity to MRS acceptance. This work presents preliminary results from the analysis of tourists’ personalities and emotions and their association with prospective MRS user-requirements using field-based data.
Initial results from this study indicate that different personalities have different user requirements that support our initial assumptions. Similarly, emotion is found as an influencing factor to user requirements. Finally, user acceptance of prospective MRS is linked to personality.
Concluding, due to the complex relationships among variables we suggest further analysis through regression and data mining using association rules and decision trees as part of our future work to identify patterns that could be used in the design of a prospective MRS for tourists.
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