Travel Recommender Systems

Tourism is a rich and growing activity in the world. The digital support of the tourist experience is typically based on Tourist Recommender Systems (TRS). Widely spread TRSs typically recommend the visit of a set of Point of Interests (POIs). Unfortunately, some of these have also proved inappropriate because they do not consider in detail the indoor visit of a POI, such as a museum, which may contain several artworks and may have limited or regulated access. In this paper, we present our ongoing work on a Context-Aware Touristic Recommender System (CATReS). It provides an outdoor recommendation to select POIs suitable for a visit, as well as an indoor recommendation to select the artwork to visit. The overall recommendations are based on contextual information coming from both visitors' profile and the POIs' contextual information (e.g., the waiting time to access the museum, the average time to visit it). Our tour recommendation system is available and is being experimented at the Uffizi Galleries (Florence, Italy).

Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as 'points'). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem's modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. Keywords Tourist trip design problem Á Scenic routes Á Orienteering problem Á Time window Á Context awareness Á Web service Á Mobile application Á Android Á Performance test Á User evaluation

Personalization of user experience through recommendations involves understanding their preferences and the context they are living in. In this work, we present a method to rank travel offers returned in response to a travel request made by a user. To give a sensible answer, we learn users' preferences over time and use them to understand travelers' needs. Our solution is based on a data-mining-based recommender system. We first design a database of historical traveler data and populate it with data generated according to rules mimicking the features of actual user profiles. These rules are then used as ground truth to validate the accuracy of the proposed learning algorithm. After performing data pre-processing, a knowledge base is set up by mining association rules from the database, which will then be used along with the travel request to assign a score to each of the potential travel offers, thus ranking them. To test the proposed methodology, we generate synthesized data according to some distributions. The results of the experiments approve the effectiveness of the proposed ranking mechanisms. Finally, we demonstrate the presentation of the ranked offers to the user via some mock-ups of the intended application.

Travelers who access the Internet through currently available web information systems often experience difficulty in selecting interesting products. This is especially true for on-the-move travelers who browse information repositories using mobile devices with poor user interfaces. Travelers suffer from an overload of information and options to consider, and they lack system support in filtering information and comparing products. In this paper, we propose an innovative approach to the problem of mapping travelers' needs to a convenient set of travel products or services. In particular, we present an on-the-move restaurant recommender system (mITR) integrated with a pre-travel planning aid system (NutKing). In this approach, firstly, the knowledge contained in a repository of past user choices is exploited to initialize the recommendation process with a set of implicit preferences. Secondly, to minimize user efforts in building a precise search query, we do not require the user to formulate a query at the beginning of the interaction, rather we involve her i n a dialogue where the traveler is encouraged to provide critiques and feedback to the system recommendations. These critiques are then incorporated by the system into a new query that tries to better model the user preferences. The approach has been validated empirically by a set of users.

Inbound tourism has been steadily increasing in Japan and has benefited the country in many ways. To provide newcomers with a more satisfying experience, trip routes can be suggested by solving the Tourist Trip Design Problem (TTDP). However, the traditional TTDP does not consider the tourists' fatigue level. Hence, the most optimal route may end up not being the most desirable route. Therefore, the goal of this research is to add the fatigue factor into the TTDP and solve the Tourist Trip Design Problem with Fatigue (TTDPF). We analyze how the optimum route changes based on different levels of fatigue sensitivity. For a person with low fatigue sensitivity, it is logical to visit more tourist attractions with less rest in between. Eventually, we aim to provide the tourists with trip routes that are more realistic and directly applicable.

Personalization of user experience through recommendations involves understanding their preferences and the context they are living in. In this work, we present a method to rank travel offers returned in response to a travel request made by a user. To give a sensible answer, we learn users' preferences over time and use them to understand travelers' needs. Our solution is based on a data-mining-based recommender system. We first design a database of historical traveler data and populate it with data generated according to rules mimicking the features of actual user profiles. These rules are then used as ground truth to validate the accuracy of the proposed learning algorithm. After performing data pre-processing, a knowledge base is set up by mining association rules from the database, which will then be used along with the travel request to assign a score to each of the potential travel offers, thus ranking them. To test the proposed methodology, we generate synthesized data according to some distributions. The results of the experiments approve the effectiveness of the proposed ranking mechanisms. Finally, we demonstrate the presentation of the ranked offers to the user via some mock-ups of the intended application.

This paper presents a real case study to determine the optimal tourist route at Langkawi Island. The Langkawi Island was selected as thecase study because normally, tourist travel to this island will drive the rented car as the primary mode of transport. Thus, the aim of this paper is to develop a mathematical model to find an optimal route for tourist to travel to their interesting places around Langkawi Island. In order to solve the problem, Greedy method was applied in this study and MATLAB version 7.8 has been used to get the solution. The result obtained shows that Nearest Greedy Insertion method gives better result compared to the Nearest Greedy method. The minimum value of the route selection gives effect to the cost of travelling. Therefore, from this study, the best route that connect from one interesting place to others place can be suggested to the tourist as guidance. In addition, tourist can save their time and money to visit all interesting places in this study.

Inbound tourism has been steadily increasing in Japan and has benefited the country in many ways. To provide newcomers with a more satisfying experience, trip routes can be suggested by solving the Tourist Trip Design Problem (TTDP). However, the traditional TTDP does not consider the tourists’ fatigue level. Hence, the most optimal route may end up not being the most desirable route. Therefore, the goal of this research is to add the fatigue factor into the TTDP and solve the Tourist Trip Design Problem with Fatigue (TTDPF). We analyze how the optimum route changes based on different levels of fatigue sensitivity. For a person with low fatigue sensitivity, it is logical to visit more tourist attractions with less rest in between. Eventually, we aim to provide the tourists with trip routes that are more realistic and directly applicable.

We present e-Turist, an intelligent system that helps tourists plan a personalised itinerary to a tourist area, taking into account individual’s preferences and limitations. After creating the route, e -Turist also offers real-time GPS guidance and audio description of points of interest visited. Here we focus on two main components, the recommender system and the route planning algorithm. We also present some use cases to highlight e-Turist functionalities in different configurations.

Product reviews or user opinions represent a peculiar information source that can be exploited in supporting travel decision making. Product reviews provide other user's experiences and, when consulted during a decision making process, tend to increase the user's confidence in the decision by providing a feeling of greater objectivity in the product description compared with commercial information. Notwithstanding their value, until now, product reviews have not been integrated in travel recommender systems, they have been used only in dedicated web sites. In this paper we present the results of a first step towards the introduction of product reviews in a travel recommender system, namely the outcome of a user behaviour survey. In this survey, we investigated the possible roles/functions of product reviews in the product selection decision process. In this paper we show that there is a significant difference in the role of reviews with respect to the product involved in the decision process (e.g. hotel vs. activity) and we demonstrate that advanced users, i.e., users who are familiar with online booking platforms and with consumer-opinion platforms, are more interested in consulting reviews that criticize the product and express a negative evaluation of it.

Travelers who access the Internet through currently available web information systems often experience difficulty in selecting interesting products. This is especially true for on-the-move travelers who browse information repositories using mobile devices with poor user interfaces. Travelers suffer from an overload of information and options to consider, and they lack system support in filtering information and comparing products. In this paper, we propose an innovative approach to the problem of mapping travelers' needs to a convenient set of travel products or services. In particular, we present an on-the-move restaurant recommender system (mITR) integrated with a pre-travel planning aid system (NutKing). In this approach, firstly, the knowledge contained in a repository of past user choices is exploited to initialize the recommendation process with a set of implicit preferences. Secondly, to minimize user efforts in building a precise search query, we do not require the user to formulate a query at the beginning of the interaction, rather we involve her i n a dialogue where the traveler is encouraged to provide critiques and feedback to the system recommendations. These critiques are then incorporated by the system into a new query that tries to better model the user preferences. The approach has been validated empirically by a set of users.

Abstract—Recently, Automated checkout shopping trolley technologies have taken an advance step than the previous introduced self-checkout system in terms of greater convenience, ease-of-use and greater efficiency. As this technology is considerable new in the market of developing countries, hence it is vital to explore the perception of consumers toward the use of automated checkout trolley. The purpose of this paper is to examine the perceptions and expectations of both enterprise users and end users toward the proposed ...

We present e-Turist, an intelligent system that helps tourists plan a personalised itinerary to a tourist area, taking into account individual’s preferences and limitations. After creating the route, e -Turist also offers real-time GPS guidance and audio description of points of interest visited. Here we focus on two main components, the recommender system and the route planning algorithm. We also present some use cases to highlight e-Turist functionalities in different configurations.

The Team Orienteering Problem with Time Windows (TOPTW) deals with deriving a number of tours comprising a subset of candidate nodes (each associated with a "profit" value and a visiting time window) so as to maximize the overall "profit", while respecting a specified time span. TOPTW has been used as a reference model for the Tourist Trip Design Problem (TTDP) in order to derive near-optimal multiple-day tours for tourists visiting a destination featuring several points of interest (POIs), taking into account a multitude of POI attributes. TOPTW is an NP-hard problem and the most efficient known heuristic is based on Iterated Local Search (ILS). However, ILS treats each POI separately; hence it tends to overlook highly profitable areas of POIs situated far from the current location, considering them too time-expensive to visit. We propose two cluster-based extensions to ILS addressing the aforementioned weakness by grouping POIs on disjoint clusters (based on geographical criteria), thereby making visits to such POIs more attractive. Our approaches improve on ILS with respect to solutions quality, while executing at comparable time and reducing the frequency of overly long transfers among POIs.

This article deals with the problem of deriving personalized recommendations for daily sightseeing itineraries for tourists visiting any destination. Our approach considers selected places of interest that a traveller would potentially wish to visit and derives a near-optimal itinerary for each day of visit; the places of potential interest are selected based on stated or implied user preferences. Our method enables the planning of customised daily personalised tourist itineraries considering user preferences, time available for visiting ...

Travelers who access the Internet through currently available web information systems often experience difficulty in selecting interesting products. This is especially true for on-the-move travelers who browse information repositories using mobile devices with poor user interfaces. Travelers suffer from an overload of information and options to consider, and they lack system support in filtering information and comparing products. In this paper, we propose an innovative approach to the problem of mapping travelers' needs to a convenient set of travel products or services. In particular, we present an on-the-move restaurant recommender system (mITR) integrated with a pre-travel planning aid system (NutKing). In this approach, firstly, the knowledge contained in a repository of past user choices is exploited to initialize the recommendation process with a set of implicit preferences. Secondly, to minimize user efforts in building a precise search query, we do not require the user to formulate a query at the beginning of the interaction, rather we involve her i n a dialogue where the traveler is encouraged to provide critiques and feedback to the system recommendations. These critiques are then incorporated by the system into a new query that tries to better model the user preferences. The approach has been validated empirically by a set of users.

We present e-Turist, an intelligent system that helps tourists plan a personalised itinerary to a tourist area, taking into account individual’s preferences and limitations. After creating the route, e -Turist also offers real-time GPS guidance and audio description of points of interest visited. Here we focus on two main components, the recommender system and the route planning algorithm. We also present some use cases to highlight e-Turist functionalities in different configurations.

Contemporary travel information services are connected to huge amount of travel related data used for improving personalized suggestions. Such suggestions include finding better routes, access to amusement and educational amenities implemented as digital services, as well as the features for people collaboration, and for planning leisure time with respect to existing attractiveness evaluation algorithms under time-budget constraints. Much effort is required for supporting personalized itineraries construction in such a way which would leverage existing cultural and technological user experience. In this paper we analyze the underlying algorithms and major components being an implementation of the proposed model investigated with particular attention to annotated leisure walk route construction, traveler collaboration and travel meeting management. In sum, we make an effort to address a number of complex issues in the area of developing models, interfaces and algorithms required by modern travel services considered as an essential application of a human-centric computing multidisciplinary paradigm.

The Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) can be used to model several real life problems. Among them, the route planning problem for tourists interested in visiting multiple points of interest (POIs) using public transportation. The main objective of this problem is to select POIs that match tourist preferences, taking into account a multitude of parameters and constraints while respecting the time available for sightseeing in a daily basis and integrating public transportation to travel between POIs (Tourist Trip Design Problem, TTDP). TDTOPTW is NP-hard while almost the whole body of the related literature addresses the non time dependent version of the problem. The only TDTOPTW heuristic proposed so far is based on the assumption of periodic transit service schedules. Herein, we propose efficient cluster-based heuristics for the TDTOPTW which yield high quality solutions, take into account time dependency in calculating travel times between POIs and make no assumption on periodic service schedules. The validation scenario for our prototyped algorithms involved the transit network and real POI datasets compiled from the metropolitan area of Athens (Greece). Our TTDP algorithms handle arbitrary (i.e. determined at query time) rather than fixed start/end locations for derived tourist itineraries.

The Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) can be used to model several real life problems. Among them, the route planning problem for tourists interested in visiting multiple points of interest (POIs) using public transport. The main objective of this problem is to select POIs that match tourist preferences, while taking into account a multitude of parameters and constraints and respecting the time available for sightseeing in a daily basis. TDTOPTW is NP-hard while almost the whole body of the related literature addresses the non time dependent version of the problem. The only TDTOPTW heuristic proposed so far is based on the assumption of periodic service schedules. Herein, we propose two efficient cluster-based heuristics for the TDTOPTW which yield high quality solutions, take into account time dependency in calculating travel times between POIs and make no assumption on periodic service schedules. The validation scenario for our prototyped algorithms included the metropolitan transit network and real POI sets compiled from Athens (Greece).

This study deals with the application of a Web structure available to both Mobility Managers and employees. Its main functionalities enable the Mobility Manager to collect mobility data that can be used to design some optimal home-to-work mobility strategies and the employees to access a car pooling service. The realisation of this Web structure has been made possible by using, as support tools, Intelligent Transportation System Technologies such as Geographic Information System and Web services beside a communication network. The realisation of the application has been preceded by a pilot survey carried out within an industrial area in South Sardinia. The survey results have highlighted the need for an organised management of the home-to-work mobility, as well as the sample's willingness to use viable alternatives to private car use. In this article, the modules related to the on line questionnaires and the car pooling service are investigated. With reference to the car pooling service, the functioning of the matching algorithm has been tested by using the pilot survey results and is described in the appendix. The testing results show that the response times for the algorithm are acceptable for home-to-work trips, within the territorial context investigated in this research.

Recently, Automated checkout shopping trolley technologies have taken an advance step than the previous introduced self-checkout system in terms of greater convenience, ease-of-use and greater efficiency. As this technology is considerable new in the market of developing countries, hence it is vital to explore the perception of consumers toward the use of automated checkout trolley. The purpose of this paper is to examine the perceptions and expectations of both enterprise users and end users toward the proposed checkout system for hypermarket in the context of Malaysia, as a typical paradigm of developing countries. Findings from this study indicate that both consumers and retailers possess positive attitude toward the automated checkout trolley system, and are enthusiastic to try this emerging technology. The main concerns of the users are the secureness of payment and the variability of payment methods, which forms their core criteria in determining whether to adopt the system. This study provides an understanding about the practicality and market condition of automated trolley system in the developing countries. -based self-service (TBSS), Automated checkout, Market survey, Technology and innovation management I. INTRODUCTION ECHNOLOGY-BASED self-services (TBSS) have gradually repositioning as one of the important mechanisms to deliver superior customer value, especially in retailing sector . Mass retailers such as hypermarkets and department stores are the pioneers and main adopters of self-service technologies (SST). In this emerging trend of technologically-oriented service, customers utilize self-service technologies in order to provide service for themselves with or without help from a retail contact employee . Studies postulate that self-service technologies create customer value by helping customer checkout more quickly, providing them a simple convenience and usually leading to higher perceived service quality .

Abstract—Recently, Automated checkout shopping trolley technologies have taken an advance step than the previous introduced self-checkout system in terms of greater convenience, ease-of-use and greater efficiency. As this technology is considerable new in the market of developing countries, hence it is vital to explore the perception of consumers toward the use of automated checkout trolley. The purpose of this paper is to examine the perceptions and expectations of both enterprise users and end users toward the proposed ...

Travelers who access the Internet through currently available web information systems often experience difficulty in selecting interesting products. This is especially true for on-the-move travelers who browse information repositories using mobile devices with poor user interfaces. Travelers suffer from an overload of information and options to consider, and they lack system support in filtering information and comparing products. In this paper, we propose an innovative approach to the problem of mapping travelers ’ needs to a convenient set of travel products or services. In particular, we present an on-the-move restaurant recommender system (mITR) integrated with a pre-travel planning aid system (NutKing). In this approach, firstly, the knowledge contained in a repository of past user choices is exploited to initialize the recommendation process with a set of implicit preferences. Secondly, to minimize user efforts in building a precise search query, we do not require the user to ...

Travelers who access the Internet through currently available web information systems often experience difficulty in selecting interesting products. This is especially true for on-the-move travelers who browse information repositories using mobile devices with poor user interfaces. Travelers suffer from an overload of information and options to consider, and they lack system support in filtering information and comparing products. In this paper, we propose an innovative approach to the problem of mapping travelers' needs to a ...

This article deals with the problem of deriving personalized recommendations for daily sightseeing itineraries for tourists visiting any destination. Our approach considers selected places of interest that a traveller would potentially wish to visit and derives a near-optimal itinerary for each day of visit; the places of potential interest are selected based on stated or implied user preferences. Our method enables the planning of customised daily personalised tourist itineraries considering user preferences, time available for visiting ...

This paper deals with the problem of deriving personalized recommendations for daily sightseeing itineraries for tourists visiting any destination. Our approach considers selected places of interest that a traveler would potentially wish to visit and derives a near-optimal itinerary for each day of visit; the places of potential interest are selected based on stated or implied user preferences. Our method enables the planning of customized daily personalized tourist itineraries considering user preferences, time available for visiting ...

PARTHENOS stands for Pooling Activities, Resources and Tools for Heritage Eresearch Networking, Optimization and Synergies. It provides a thematic cluster of European Research Infrastructures, integrating initiatives, e-infrastructures and other world-class infrastructures, and builds bridges and provides common solutions to similar problems, between di↵erent, tightly interrelated fields. The project, funded by the EC in the framework of H2020 Programme, is creating a common ecosystem to support Humanities, Language, Cultural Heritage, History and Archaeology Research. The project is driving interoperability of tools, applicability of common standards, shared policies on IPR, Open data and Open Access. The ICCU has the task to identify the user requirements of the many communities involved, such as History, Language Studies, Social Science, Cultural Heritage, Archaeology, and related fields across the (Digital) Humanities, about data policies and to implement common policies and str...

When a tourist visits a city, they tend to visit as many touristic places in the city as possible within their time limit. So, for optimization of their limited time budget and allowing them to spend their maximum time in the destination, this paper proposes a model to find an optimal route for the destination points a tourist is interested into. The computer model developed in this paper makes a sub-tour within the original network and search for an optimal tour route hence giving total travel time and total time required to visit desired destinations, and returning back to the place where you started from. The model enables fast decision support for tourists. The model developed is then applied in network of Pokhara and optimal route is found for a sample set of destinations.

a,* Department of Industrial Engineering, Faculty of Materials and Industrial Engineering, Babol Noshirvani, University of Technology, Babol, Iran, (ali.divsalar@nit.ac.ir) b Department of Industrial Engineering, Faculty of Materials and Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran c Department of Industrial Engineering, Faculty of Materials and Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran

Technology is finding its feet in the travel industry in last few years. This advancement in the field of technology has led to endless sources of tourism related information. Social media like Facebook is one of such sources. People share their experience and reviews on Facebook on a daily basis. This paper proposes a system which automates the dreaming stage, i.e., suggesting a destination to the tourist. Our main contribution is suggesting destination to the tourist by studying his Facebook profile. A separate recommendation system is designed which suggests destination using collaborative filtering i.e. based on other tourist's experience. The user profile is then compared with other tourist profiles. The recommendation is provided based on choices made by the other user whose profile matches with the target user.

Planning a tourist trip in a foreign city can be a complex undertaking: when selecting the attractions and choosing visit order and visit durations, opening hours as well as the public transit timetable need to be considered. Additionally, when planning trips for multiple days, it is desirable to avoid redundancy. Since the attractiveness of activities such as shopping or sightseeing depends on personal preferences, there is no one-size-fits-all solution to this problem. We propose several realistic extensions to the Time-Dependent Team Orienteering Problem with Time Windows (TDTOPTW) which are relevant in practice and present the first MILP representation of it. Furthermore, we propose a problem-specific preprocessing step which enables fast heuristic (iterated local search) and exact (mixed-integer linear programming) personalized trip-planning for tourists. Experimental results for the city of Berlin show that the approach is feasible in practice. 2012 ACM Subject Classification Mathematics of computing → Combinatorial optimization; Mathematics of computing → Graph algorithms; Theory of computation → Randomized local search Keywords and phrases tourist tour planning, orienteering problem, TDTOPTW, mixed integer linear programming, iterated local search, computational study

The aim of this paper is to study a Bike Sharing Touring (BST) applying a mathematical model known in operation research as Orienteering Problem (OP). Several European Cities are developing BST in order to reduce the exhaust emissions and to improve the sustainability in urban areas. The authors offer a Decision Support Tool useful for the tourist and the service’s manager to organize the tourists’ paths on the basis of tourists’ desires, subject to usable time, place of interest position and docking station location. The model analyzed presents two innovative aspects compared to a classic OP. The first one is that the start and the arrival point of routes aren’t necessary coinciding and pre-conditioned. The last one is that the knowledge of tourist tours allows to book the visit to a point of interest and doing so to optimize efficiency of the whole system and not only of the single tourist tour.

PARTHENOS stands for Pooling Activities, Resources and Tools for Heritage Eresearch Networking, Optimization and Synergies. It provides a thematic cluster of European Research Infrastructures, integrating initiatives, e-infrastructures and other world-class infrastructures, and builds bridges and provides common solutions to similar problems, between di↵erent, tightly interrelated fields. The project, funded by the EC in the framework of H2020 Programme, is creating a common ecosystem to support Humanities, Language, Cultural Heritage, History and Archaeology Research. The project is driving interoperability of tools, applicability of common standards, shared policies on IPR, Open data and Open Access. The ICCU has the task to identify the user requirements of the many communities involved, such as History, Language Studies, Social Science, Cultural Heritage, Archaeology, and related fields across the (Digital) Humanities, about data policies and to implement common policies and str...

We present e-Turist, an intelligent system that helps tourists plan a personalised itinerary to a tourist area, taking into account individual’s preferences and limitations. After creating the route, e -Turist also offers real-time GPS guidance and audio description of points of interest visited. Here we focus on two main components, the recommender system and the route planning algorithm. We also present some use cases to highlight e-Turist functionalities in different configurations.

Personalization of user experience through recommendations involves understanding their preferences and the context they are living in. In this work, we present a method to rank travel offers returned in response to a travel request made by a user. To give a sensible answer, we learn users' preferences over time and use them to understand travelers' needs. Our solution is based on a data-mining-based recommender system. We first design a database of historical traveler data and populate it with data generated according to rules mimicking the features of actual user profiles. These rules are then used as ground truth to validate the accuracy of the proposed learning algorithm. After performing data pre-processing, a knowledge base is set up by mining association rules from the database, which will then be used along with the travel request to assign a score to each of the potential travel offers, thus ranking them. To test the proposed methodology, we generate synthesized data according to some distributions. The results of the experiments approve the effectiveness of the proposed ranking mechanisms. Finally, we demonstrate the presentation of the ranked offers to the user via some mock-ups of the intended application.

One of the problems tourism faces is how to make itineraries more effective and efficient. This research has solved the routing problem with the objective of maximizing the score and minimizing the time needed for the tourist’s itinerary. Maximizing the score means collecting a maximum of various kinds of score from each destination that is visited. The profits differ according to whether those destinations are the favorite ones for the tourists or not. Minimizing time means traveling time and visiting time in the itinerary being kept to a minimum. Those are small case with 16 tourism destinations in East Java, and large case with 56 instances consists of 100 destinations each from previous research. The existing model is the Team Orienteering Problem with Time Window (TOPTW), and the development has been conducted by adding another objective, minimum time, become Flexible TOPTW. This model guarantees that an effective itinerary with efficient timing to implement will be produced. M...

One of the problems tourism faces is how to make itineraries more effective and efficient. This research has solved the routing problem with the objective of maximizing the score and minimizing the time needed for the tourist's itinerary. Maximizing the score means collecting a maximum of various kinds of score from each destination that is visited. The profits differ according to whether those destinations are the favorite ones for the tourists or not. Minimizing time means traveling time and visiting time in the itinerary being kept to a minimum. Those are small case with 16 tourism destinations in East Java, and large case with 56 instances consists of 100 destinations each from previous research. The existing model is the Team Orienteering Problem with Time Window (TOPTW), and the development has been conducted by adding another objective, minimum time, become Flexible TOPTW. This model guarantees that an effective itinerary with efficient timing to implement will be produced. Modification of Iterated Local Search (ILS) into Adjustment ILS (AILS) has been done by replacing random construction in the early phase with heuristic construction, continue with Permutation, Reserved and Perturbation. This metaheuristic method will address this NP-hard problem faster than the heuristic method because it has better preparation and process. Contributing to this research is a multi-objective model that combines maximum score and minimum time, and a metaheuristics method to solve the problem faster and effectively. There are calibration parameter with 17 instances of 100 destinations each, small case test using Mixed Integer Linear Programming, and large case test comparing AILS with Multi-Start Simulated Annealing (MSA), Simulated Annealing (SA), Artificial Bee Colony (ABC), and Iterated Local Search. The result shows that the proposed model will provide itinerary with less number of visited destination 4.752% but has higher total score 8.774%, and 3836.877% faster, comparing with MSA, SA, and ABC. While AILS is compared with ILS, it has less visited destination 5.656%, less total score 56.291%, and faster 375.961%. Even though AILS has more efficient running time than other methods, it needs improvement in algorithm to create better result.

One of the problems tourism faces is how to make itineraries more effective and efficient. This research has solved the routing problem with the objective of maximizing the score and minimizing the time needed for the tourist's itinerary. Maximizing the score means collecting a maximum of various kinds of score from each destination that is visited. The profits differ according to whether those destinations are the favorite ones for the tourists or not. Minimizing time means traveling time and visiting time in the itinerary being kept to a minimum. Those are small case with 16 tourism destinations in East Java, and large case with 56 instances consists of 100 destinations each from previous research. The existing model is the Team Orienteering Problem with Time Window (TOPTW), and the development has been conducted by adding another objective, minimum time, become Flexible TOPTW. This model guarantees that an effective itinerary with efficient timing to implement will be produced. Modification of Iterated Local Search (ILS) into Adjustment ILS (AILS) has been done by replacing random construction in the early phase with heuristic construction, continue with Permutation, Reserved and Perturbation. This metaheuristic method will address this NP-hard problem faster than the heuristic method because it has better preparation and process. Contributing to this research is a multi-objective model that combines maximum score and minimum time, and a metaheuristics method to solve the problem faster and effectively. There are calibration parameter with 17 instances of 100 destinations each, small case test using Mixed Integer Linear Programming, and large case test comparing AILS with Multi-Start Simulated Annealing (MSA), Simulated Annealing (SA), Artificial Bee Colony (ABC), and Iterated Local Search. The result shows that the proposed model will provide itinerary with less number of visited destination 4.752% but has higher total score 8.774%, and 3836.877% faster, comparing with MSA, SA, and ABC. While AILS is compared with ILS, it has less visited destination 5.656%, less total score 56.291%, and faster 375.961%. Even though AILS has more efficient running time than other methods, it needs improvement in algorithm to create better result.

Abstract—Recently, Automated checkout shopping trolley technologies have taken an advance step than the previous introduced self-checkout system in terms of greater convenience, ease-of-use and greater efficiency. As this technology is considerable new in the market of developing countries, hence it is vital to explore the perception of consumers toward the use of automated checkout trolley. The purpose of this paper is to examine the perceptions and expectations of both enterprise users and end users toward the proposed ...

Abstract—Recently, Automated checkout shopping trolley technologies have taken an advance step than the previous introduced self-checkout system in terms of greater convenience, ease-of-use and greater efficiency. As this technology is considerable new in the market of developing countries, hence it is vital to explore the perception of consumers toward the use of automated checkout trolley. The purpose of this paper is to examine the perceptions and expectations of both enterprise users and end users toward the proposed ...

When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist agenda. Finally, they fine tune up this route to better fit tourists' needs. We present an intelligent routing system to fulfil the same task. We divide this process in three steps: recommendation, route generation and route customization. We focus on the last two steps and analyze them. We model the tourist planning problem, integrating public transportation, as the Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) and we present an heuristic able to solve it on real-time. Finally, we show the prototype which generates and customizes routes in real-time.

Due to the high complexity of the required calculations, Intelligent Routing Systems have to apply latest Operations Research techniques to be able to create routes efficiently. This paper proposes a solution to the Multi Path Orienteering Problem with ...

This study concerns the optimal design of cycle tourist itineraries considering several classes of users. It builds upon a recent work which first introduced the problem of designing the most attractive itinerary for cycle tourists connecting a given origin to a given destination, subject to a budget and a time constraint. Starting from a network made of existing cycle-trails, gravel paths, and unsurfaced field roads, local administrators face the problem of selecting a budget-compliant set of edges to be reconditioned and turned into paved bike trails. Indeed, investing in enhancing cycle tourism infrastructures proved to be effective in fostering sustainable development but decision support tools are needed to support decision makers in optimizing scarce public resources spending. The key issue in this problem is the objective function, namely the route attractiveness. Each node and each edge of the network yields a reward each time it is traversed, related to the pleasure of cycling along it. Additional pleasure usually decreases when traversing the same edge or node one more time but it may still be positive. Therefore, the optimal route may contain cycles, which is a special feature of this problem. In previous studies attractiveness was computed on the basis of each point of interest located on the edges and at the nodes of the route, and the route maximizing total attractiveness was searched for. The focus was on the generalist cycle tourist, without thematic preferences. This study takes a more realistic view and proposes a model where different classes of cycle tourist are considered individually, each one with its own preferences, like the cultural oriented tourist, the gastronomic fan, or the one fond of wild life and nature. This new perspective yields a new network design problem in the field of vehicle routing problems with profits, generalizing the Orienteering Problem, that we call the multi-commodity orienteering problem with network design (MOP-ND): it consists of designing a set of itineraries, one for each user class, sharing the same origin and the same destination and potentially any edge of the network, so that each itinerary satisfies a maximum duration constraint and the cost of the whole infrastructure is budget compliant. The objective is to maximize the sum on all user classes of the attractiveness of the itinerary selected for that class. In this paper we provide a mathematical model for MOP-ND, test it on realistic data, and compare with the generalist model.

ROSE (ROuting SErvice) is an application for mobile phones, which suggests events and locations to the user and guides him to them via public transportation. Many different systems partly incorporating recommending and navigation features exist. However, no system exists which combines event recommendation and pedestrian navigation with (live) public transport support. In this paper we describe ROSE a mobile application which combines these features. Our motivation is to free the passenger from many tedious tasks, e.g. finding an interesting event and navigating to it. ROSE determines the best possible transport link and then accompanies the passenger throughout his entire journey. It reacts in real time to delays in the public transport system and calculates alternative routes when necessary. For route planning in this context, we will propose a h u-optimal algorithm for incorporating non-monotone multi dimensional user preferences in an A*-like algorithm. We also present an assignment of theoretical foundations to real world route planning problems.

Routing algorithms are traditionally considered to apply the sum of profits gathered at visited locations as an objective function since the Traveling Salesman Problem. This heritage disregards many practical considerations, hence the result of these models meet with user's needs rarely. Thus considering the importance of this theoretical and modeling problem, a novel objective function will be presented in this paper as an extension of the one inherited from the TSP that is more aligned with user preferences and aims to maximize the tourist's satisfaction. We also propose a heuristic algorithm to solve the Team Orienteering Problem with relatively low computation time in case of high number of vertices on the graph and multiple tour days. Based on the key performance indicators and user feedback the algorithm is suitable to be implemented in a GIS application considering that even a 3-day tour is designed less than 4 seconds.

The Orienteering Problem (OP) is a combinatorial routing problem of which the goal is to find a tour that maximises the total score earned by visiting vertices. A set of vertices is given, determined by their coordinates and a score. The pairwise travel times between the vertices are

Tourists become increasingly dependent on mobile city guides to locate tourist services and retrieve information about nearby points of interest (POIs) when visiting unknown destinations. Although several city guides support the provision of personalized tour recommendations to assist tourists visiting the most interesting attractions, existing tour planners only consider walking tours. Herein, we introduce eCOMPASS, a context-aware mobile application which also considers the option of using public transit for moving around. Far beyond than just providing navigational aid, eCOMPASS incorporates multimodality (i.e. time dependency) within its routing logic aiming at deriving nearoptimal sequencing of POIs along recommended tours so as to best utilize time available for sightseeing and minimize waiting time at transit stops. Further advancing the state of the art, eCOMPASS allows users to define arbitrary start/end locations (e.g. the current location of a mobile user) rather than choosing among a fixed set of locations. This paper describes the routing algorithm which comprises the core functionality of eCOMPASS and discusses the implementation details of the mobile application using the metropolitan area of Berlin (Germany) as case study.