Tourism Recommender System Research Articles

A Semantic Social Recommender System Using Ontologies Based Approach For Tunisian Tourism

Tunisia is well placed in terms of medical tourism and has highly qualified and specialized medical and surgical teams. Integrating social networks in Tunisian medical tourism recommender systems can result in much more accurate recommendations. That is to say, information, interests, and recommendations retrieved from social networks can improve the prediction accuracy. This paper aims to improve traditional recommender systems by incorporating information in social network; including user preferences and influences from social friends. Accordingly, a user interest ontology is developed to make personalized recommendations out of such information. In this paper, we present a semantic social recommender system employing a user interest ontology and a Tunisian Medical Tourism ontology. Our system can improve the quality of recommendation for Tunisian tourism domain. Finally, our social recommendation algorithm is implemented in order to be used in a Tunisia tourism Website to assist users interested in visiting Tunisia for medical purposes.

Building an Image Annotation and Tourism Recommender System

The interest in image annotation and recommendation has been increased due to the ever rising amount of data uploaded to the web. Despite the many efforts undertaken so far, accuracy or efficiency still remain open problems. Here, a complete image annotation and tourism recommender system is proposed. It is based on the probabilistic latent semantic analysis (PLSA) and hypergraph ranking, exploiting the visual attributes of the images and the semantic information found in image tags and geo-tags. In particular, semantic image annotation resorts to the PLSA, exploiting the textual information in image tags. It is further complemented by visual annotation based on visual image content classification. Tourist destinations, strongly related to a query image, are recommended using hypergraph ranking enhanced by enforcing group sparsity constraints. Experiments were conducted on a large image dataset of Greek sites collected from Flickr. The experimental results demonstrate the merits of the proposed model. Semantic image annotation by means of the PLSA has achieved an average precision of 92% at 10% recall. The accuracy of content-based image classification is 82, 6%. An average precision of 92% is measured at 1% recall for tourism recommendation.

Tweet-mapping Method for Tourist Spots Based on Now-Tweets and Spot-photos

Tourism recommender systems suggest suitable tourist spots by matching the characteristics of the tourist spots with those of the user. In this paper, we focus on an essential source of these characteristics—geotagged tweets. To solve the problem of associating geotagged tweets to tourist spots, we propose a mapping method that infers the region of a target spot on the basis of two geotagged items. The first is a geotagged tweet, which demonstrates that the tweeter was indeed at the target spot at the time the tweet was posted. We call this a “now-tweet.” The second item is a geotagged photo of the target spot, which we call a “spot-photo.” We regard these now-tweets and spot-photos as training data, and then determine the region of the tourist spot by inferring the geographical distribution of the training data. Next, we map geotagged tweets from the extracted region to the target spot. To improve the accuracy with which the tourist spot is inferred, we apply a clustering algorithm to the training data. Experimental results indicate that photo-based mapping with sophisticated training data produces the most improved performance over baseline methods. When applied to 4,559,643 geotagged tweets, our method maps them to tourist spots with an average granularity of 144.85 m.

Adaptive tourist recommendation system: conceptual frameworks and implementations

Recommendation systems and adaptive systems have been introduced in travel applications to support the travelers in their decision-making processes. These systems should respond to the unexpected changes during the travel. As a result, the first step is to sense the traveler's specifications, needs, and preferences before, during, and after the travel. Moreover, they should gather information about the accommodations, flights, cities, activities, and destinations through the different sources. In the next stage, these systems should provide personalized information. However, current tourist systems are not able to collect all travelers and travel products information from different resources. In addition, they failed to provide a sequence of recommendation on the different travel products (i.e., main destination, desired budget, length of travel, accommodation, transportation, activity, and restaurant) based on the traveler order preferences and travel stage. These systems do not support any customization. For instance, expert traveler or system admin could not change the recommendation algorithm settings anymore. To address these problems and issues, we propose and implement an adaptive tourist recommendation system that is supported by an adaptive tourist recommendation framework, process, architecture, and system.

Less is more: empirical design criteria for a tourist place recommendation service which decelerates the visiting experience

Tourist recommendation services provide suggestions for places of interest. While the spatial structure of a tourist's visit has been successfully exploited to improve the quality of place recommendations, its temporal structure has not been taken into account so far. This is surprising because the importance of the visitors' time management is well known from tourism research. We present findings of an empirical study that sheds light on the temporal aspects of tourist exploration behaviour and discuss implications for the design of tourist-recommender systems. The data-set consists of interviews, GPS tracks and geo-referenced photo sequences from visitors of a typical Middle European destination for cultural tourism. Among the results relevant to place recommending are the following: (1) the set of places is often replanned during the visit; (2) comparing place popularity based on photo frequency and place popularity based on staying time, we found notable differences in rank ordering; and (3) the photo frequency and the detour sinuosity of the GPS tracks are decreasing slowly over time. We interpret this last finding as a time-geographic cone effect. Based on the empirical results, we discuss the implications for the design of tourist-recommending services and propose a corresponding user interface utilising a recommendation strategy that tries to counterbalance the time-geographic cone effect, that is, to decelerate the visiting experience.

A Novel Approach towards Tourism Recommendation System with Collaborative Filtering and Association Rule Mining

In the tourism recommendation system, the number of users and items is very large. But traditional recommendation system uses partial information for identifying similar characteristics of users. Collaborative filtering is the primary approach of any recommendation system. It provides a recommendation which is easy to understand. It is based on similarities of user opinions like rating or likes and dislikes. So the recommendation provided by collaborative cannot be considered as quality recommendation. Recommendation after association rule mining is having high support and confidence level. So that will be considered as strong recommendation. The hybridization of both collaborative filtering and association rule mining can produce strong and quality recommendation even when sufficient data are not available. This paper combines recommendation for tourism application by using a hybridization of traditional collaborative filtering technique and data mining techniques.

A Modified Approach towards Tourism Recommendation System with Collaborative Filtering and Association Rule Mining

In the tourism recommendation system, the number of users and items is very large. But traditional recommendation system uses partial information for identifying similar characteristics of users. Collaborative filtering is the primary approach of any recommendation system. It provides a recommendation which is easy to understand. It is based on similarities of user opinions like rating or likes and dislikes. So the recommendation provided by collaborative cannot be considered as quality recommendation. Recommendation after association rule mining is having high support and confidence level. So that will be considered as strong recommendation. The hybridization of both collaborative filtering and association rule mining can produce strong and quality recommendation even when sufficient data are not available. This paper combines recommendation for tourism application by using a hybridization of traditional collaborative filtering technique and data mining techniques.

A real-time personalized route recommendation system for self-drive tourists based on vehicle to vehicle communication

Recently, traffic jams and long queuing problems in tourist hot spots is growing with the increasing number of self-drive tourists. Some recommendation systems have been developed in attempt to relieve these problems. However, all these systems lack information pertaining to real-time traffic as well as the ability of personalization. In this research, we have developed a novel route recommendation system to provide self-drive tourists with real-time personalized route recommendations. This will help to reduce the traffic jams and queuing time in tourist hot spots. It will also help to personalize visiting routes based on the user’s specific preferences. Ultimately, based on the evaluation results given by experienced self-drive tourists, we have shown that the proposed system not only saves total visiting time, but also meets their specific visiting preferences.

Mining Context History for Generating User Models for Proactive Personalized Mobile Networking Applications

Context history has a concrete role to play in supporting proactive personalized services in a mobile and ubiquitous networking environment. However, despite its promise, the utilization of context history in this way is a relatively under-explored research field. This paper proposed a context-aware system framework for proactive personalized mobile networking applications based on user models utilizing the context history. Based on the proposed framework, we presented a system called CAMTRS, which was an context-aware mobile tourism recommender system that serves a tourist with information needed in his specific context that are interesting to him given his goal for that moment. From our experiment and evaluation, the proposed framework is a promising approach to provider proactive personalized services to mobile users.

Lightweight tourism recommendation

In this paper, we present the tours planning system entitled TOURSPLAN, along with a new lightweight user modelling UM process intended to work as a tourism recommendation system in a commercial environment. The new process tackles issues like cold start, grey sheep and over-specialisation through a rich user model and the application of a gradual forgetting function to the collected user action history. Also, significant performance improvements were achieved regarding the previously proposed UM process.

Designing a Tourism Recommender System Based on Location, Mobile Device and User Features in Museum

Today, mobile devices are widely used by many tourists. They can use mobile for accessing information about all sightseeing around the world. On the other hand, it seems essential to personalize the content due to diversity of learners and variation of the tools they use. On the whole, the goal for personalization is to suggest a collection of comprehensive activities, taking into consideration factors such as location, user preferences and interests and so on. One of the applications of recommender systems is in tourism industry. Mobile devices can be used in sites such as museums. Demands of different people such as students, tourists and ordinary people can be met in spite of their diverse features and preferences. The purpose of this paper is to give a model which is able to recommend new locations to visitors in a museum while the visitor is given essential information about the certain features of the suggested site.

Empirical Evidence of Contribution to E-Tourism by Application of Personalized Tourism Recommendation System

This article examines the necessity of applying recommendations by introducing web portal in the line of enriching e-tourism. It argues the dramatically change of tourist motives and interests due to the rapid usage of Internet. In order to cope with ever-changing tourist preferences and to contribute to tourism and economic development, a tourism recommendation system is proposed. Hence, a software module is developed based on collaborative filtering method which accuracy testing performed satisfactory outcomes. Moreover, acting as a national tourism web portal it enables to be introduced as a tool for assisting tourists in identification of a tailor-made tourist place. So, the study indicates a solution for generating personalized list of interesting tourism items by matching them to the tourists’ preferences. In this line, “MyTravelPal” is strongly recommended as a way-out in increasing the awareness for certain tourist destination that is capable of fulfilling travelers’ favorites.

Evaluation of a web recommender system in electronic and mobile tourism

Recommender systems are software frameworks that employ a specific type of information filtering technique, aiming at recommending information items or social elements that are likely to be of interest to the user. Herein, we present the evaluation results of the recently prototyped tourism recommender system (TRS). We followed a formal evaluation process to validate the usability of two versions of the TRS by users in realistic environments; the first is a typical web recommender system (wTRS), while the second is a system that enables tourist content recommendations addressed to mobile users (mTRS). The usability evaluation tests have been undertaken at the municipality of Mytilene, Greece.

Bayesian intelligent semantic mashup for tourism

A common perception is that there are two competing vision for the future evolution of the Web: the Semantic Web and Web 2.0. In fact, Semantic Web technologies must integrate with Web 2.0 services for both to leverage each other’s strengths. This paper illustrates how Semantic Web technologies can support information integration and make it easy to create semantic mashups. An intelligent recommendation system for tourism is presented to show the efficiency of our method. Through the ontology of tourism, the system allows the integration of heterogeneous online travel information. An integrated knowledge process is developed to guarantee the whole engineering procedure. Based on Bayesian network technique, the system recommends tourist attractions to a user by taking into account the travel behaviour both of the user and of other users.

Bayesian intelligent semantic mashup for tourism

AbstractA common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. In fact, Semantic Web technologies must integrate with Web 2.0 services for both to leverage each other's strengths. This paper illustrates how Semantic Web technologies can support information integration and make it easy to create semantic mashups. An intelligent recommendation system for tourism is presented to show the efficiency of our method. Through the ontology of tourism, the system allows the integration of heterogeneous online travel information. An integrated knowledge process is developed to guarantee the whole engineering procedure. Based on the Bayesian network technique, the system recommends tourist attractions to a user by taking into account the travel behavior both of the user and of other users. Copyright © 2010 John Wiley & Sons, Ltd.

Exploiting digital TV users' preferences in a tourism recommender system based on semantic reasoning

Tourism recommender systems match the user preferences against the huge diversity of tourist resources, helping to decide where to go and what to do. Current approaches require the users to initialize manually their profiles by expressing their interests accurately, which is a very tedious process. We propose a system that automatically infers the users' preferences from their TV viewing histories, i.e., the tourism resources the users might appreciate are selected by considering the TV contents they enjoyed in the past. To this aim, we have developed a context-aware semantics-based recommendation strategy that considers both the users' preferences and the interests of like-minded individuals. The resulting recommendations shape a tailor-made on-move travel plan the users can access via (domestic and) handheld consumer devices.

Template-Based Adaptation of Semantic Web Services with Model-Driven Engineering

Service-oriented enterprise systems, which tend to be heterogeneous, loosely coupled, long-lived, and continuously running, have to cope with frequent changes to their requirements and the environment. In order to address such changes, applications need to be inherently flexible and adaptive, supported by appropriate infrastructures. In this paper, we propose a model-driven approach for the dynamic adaptation of Web services based on ontology-aware service templates. Model-driven engineering raises the level of abstraction from concrete Web service implementations to high-level service models, which leads to more flexible and automated adaptations through template designs and transformations. The ontological semantics enhances the service matching capabilities required by the dynamic adaptation process. Service templates are based on OWL-S descriptions and provide the necessary means to capture and parameterize specific behavior patterns of service models. In this paper, we apply our approach in the context of the EU-funded ALIVE project and illustrate, as an example, how the proposed framework supports the adaptation of the authentication mechanism used by an interactive tourist recommendation system.

A Tourism Recommender Agent: from theory to practice.

In this paper a multiagent Tourism Recommender System is presented. This system has a multiagent architecture and one of its main agents, the Travel Assistant Agent (T-Agent), is modelled as a graded BDI agent. The graded BDI agent model allows us to specify an agent architecture able to deal with the environment uncertainty and with graded mental attitudes. We focus on the implementational aspects of the multiagent system and specially on the T-Agent development, going from the theoretical agent model to a concrete agent implementation.