TripBuilder: A Tool for Recommending Sightseeing Tours

Knowledge and Information Systems

Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select Points of Interest (POI) to visit in unfamiliar cities, and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photos. Our tour recommendation problem is modelled using a formulation of the Orienteering problem, and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations, and demonstrate how POI visit duration can be personalized using this time-based user interest. Furthermore , we demonstrate how PersTour can be further enhanced by: (i) a weighted updating of user interests based on the recency of their POI visits; and (ii) an automatic weighting between POI popularity and user interests based on the tourist's activity level. Using a Flickr dataset of ten cities, our experiments show the effectiveness of PersTour against various collaborative filtering and greedy-based baselines, in terms of tour popularity, interest, recall, precision and F1-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.

Proceedings of the 2015 SIGMOD PhD Symposium (SIGMOD'15), 2015

Photo sharing sites like Flickr and Instagram have grown increasingly popular in recent years, resulting in a large amount of uploaded photos. In addition, these photos contain useful meta-data such as the taken time and geo-location. Using such geo-tagged photos and Wikipedia, we propose an approach for recommending tours based on user interests from his/her visit history. We evaluate our proposed approach on a Flickr dataset comprising three cities and find that our approach is able to recommend tours that are more popular and comprise more places/points-of-interest, compared to various baselines. More importantly, we find that our recommended tours reflect the ground truth of real-life tours taken by users, based on measures of recall, precision and F1-score.

Extended Proceedings of the 27th ACM Conference on Hypertext and Social Media (HT'16), 2016

Touring is a popular but time-consuming activity, due to the need to identify interesting attractions or Places-of-Interest (POIs) and structure these POIs in the form of a time-constrained tour itinerary. To solve this challenge, we propose the Personalized Tour Recommendation and Planning (PersTour) system. The PersTour system is able to plan for a customized tour itinerary where the recommended POIs and visit durations are personalized based on the tourist's interest preferences. In addition, tourists have the option to indicate their trip constraints (e.g., a preferred start-ing/ending location and a specific tour duration) to further customize their tour itinerary.

Information and Communication Technologies in Tourism 2007, 2007

The World Wide Web provides information for most tourist locations and thus has enormous influence on tourists' decisions. However, Web applications rarely support all phases of tour planning. The city of Görlitz offers website visitors an opportunity to plan individual city tours in advance by specifying personal interests and tour preferences. Using this information, the system creates a personalized tour plan with content and navigational information displayed online or sent per email as a PDF document. Analyses of the usage data indicate that most of the average use time of 9 minutes is spent looking at the recommended tour, which suggests that users had a general interest in the recommendations made. Based on the overall use behavior, two kinds of users were identified: concrete planners and destination explorers. A comparison of the web-based application with the mobile version suggests the Web a more user-friendly environment than mobile devices.

e-review of tourism research, 2019

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/ivoyage-interactive-tour-recommendation-system https://www.ijert.org/research/ivoyage-interactive-tour-recommendation-system-IJERTCONV9IS07029.pdf Planning for a vacation that takes into consideration all the travel preferences of an individual can be tedious. An interactive tourism recommendation system is designed to provide travel itineraries to users by applying different machine learning models. This design involves taking into consideration user's preferences regarding attraction spots, cuisine type etc. Restricted Boltzmann Machine model, content-based filtering model, location-based context-aware model and predictive analysis are used in this application. The project also utilizes crowd information to support tour planning by displaying a bar graph of relative crowdedness in a day and letting users change their itinerary accordingly in order to produce a revised itinerary plan. In the proposed model we use sentiment analysis to aggregate the preferences and opinions of each individual of the tour group to provide an itinerary plan for a group.

2011 11th International Conference on Intelligent Systems Design and Applications, 2011

The evolution of tourism websites is converging to a set of features and best practices that are becoming standard, in such a way that developing a template and later customizing it to a given tourist region is becoming feasible. A very important feature in this kind of site is showing the touristic suggestions of what the tourist can find in the destination, optimally personalized for him/her. One problem in deploying such functionality however is the lack of user experience data suited to perform data mining when a new site is launched. This paper proposes a solution, customizable to any touristic region, that harnesses the information available in Flickr, crossing it with a Point of Interest (POI) database and using Google Prediction API (Application Programming Interface) to generate personalized travel suggestions, based on the geographical itinerary the user defined with a trip planner tool.

Extended Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP'16), Doctoral Consortium, 2016

Travel itinerary recommendation is an important but challenging problem, due to the need to recommend captivating Places-of-Interest (POI) and construct these POIs as a connected itinerary. Another challenge is to personalize these recommended itineraries based on tourist interests and their preferences for starting/ending POIs and time/distance budgets. Our work aims to address these challenges by proposing algorithms to recommend personalized travel itineraries for both individuals and groups of tourists, based on their interest preferences. To determine these interests, we first construct tourists' past POI visits based on their geo-tagged photos and then build a model of user interests based on their time spent visiting each POI. Experimental evaluation on a Flickr dataset of multiple cities show that our proposed algorithms out-perform various baselines in terms of recall, precision, F1-score and other heuristics-based metrics.

2021

Planning for a vacation that takes into consideration all the travel preferences of an individual can be tedious. An interactive tourism recommendation system is designed to provide travel itineraries to users by applying different machine learning models. This design involves taking into consideration user’s preferences regarding attraction spots, cuisine type etc. Restricted Boltzmann Machine model, contentbased filtering model, location-based context-aware model and predictive analysis are used in this application. The project also utilizes crowd information to support tour planning bydisplaying a bar graph of relative crowdedness in a day and letting users change their itinerary accordingly in order to produce a revised itinerary plan. In the proposed model we use sentiment analysis to aggregate the preferences and opinions of each individual of the tour group to provide an itinerary plan for a group. Keywords—Tour recommendation system, Restricted Boltzmann Machine, Content-bas...

SIGSPATIAL Special, 2015

We present an application where semantically enriched trajectories obtained from crowdsensed data are used to build an advanced system for planning personalized sightseeing tours, called TRIPBUILDER. The interesting feature of TRIPBUILDER is that it uses Wikipedia content and trajectories of previous tourists collected by georeferenced Flickr photos in a complex spatio-temporal framework. The objective is to address, in an unsupervised way, the problem of suggesting a budgeted sightseeing tour based on the preferences of the tourist and the time available for the visit. We present few highlights of how TRIPBUILDER works along with a research agenda where we discuss the role of semantically enriched trajectories and crowdsourced location data in planning itineraries.