RecTour: A Recommender System for Tourists (original) (raw)
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A trajectory-based recommender system for tourism
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Recommendation systems provide focused information to users on a set of objects belonging to a specific domain. The proposed recommender system provides personalized suggestions about touristic points of interest. The system generates recommendations, consisting of touristic places, according to the current position of a tourist and previously collected data describing tourist movements in a touristic location/city. The touristic sites correspond to a set of points of interest identified a priori.
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.
Personalized travel suggestions for tourism websites
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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.
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This paper introduces a recommendation system which recommends the tourist location to the users, based on their own input preference stated. Input preference is in terms of point of interest, climate, age and budget. This recommendation system uses a dynamic database for recommending the users. Database is created out of survey conducted. Survey is taken from the diverse travelers where they fill their previous tourist trip point of interest, budget, cuisine, climate etc. By matching the user input preference against the survey data stored in database, the output location is recommended.
Interesting Place Recommender System for Tourists
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Travel is a combination of different aspects which are likely to be experienced by most people in their life. Nowadays, information related to all the travel aspects is usually available on the Internet and some travel websites will provide travel packages for users. People will usually seek help when planning a trip. However, existing travel websites do not have a suggestion facility that can suggest suitable places based on the users’ needs and requirements. Hence, users found it difficult to plan their vacation. This project is aimed at developing an interesting place recommender system for tourists based on their background. In this project, a website application known as the Interesting Place Recommender System for Tourists (M’ Tourism) was developed. It is a website application that generates a list of recommended places for users based on their background. In more detail, this recommender system will match the users’ background that are deposited in this system and suggest a ...
Personalized Recommendation of Travel Itineraries based on Tourist Interests and Preferences
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.
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Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '13, 2013
The purpose of the Contextual Suggestion track, an evaluation task at the TREC 2012 conference, is to suggest personalized tourist activities to an individual, given a certain location and time. In our content-based approach, we collected initial recommendations using the location context as search query in Google Places. We first ranked the recommendations based on their textual similarity to the user profiles. In order to improve the ranking of popular sights, we combined the initial ranking with rankings based on Google Search, popularity and categories. Finally, we performed filtering based on the temporal context. Overall, our system performed well above average and median, and outperformed the baseline -Google Places only -run.
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The major objective of any Travel Recommendation System is to recommend its users to visit the most suitable place in according to the selected location. We present this system of travel recommendation from the experiences of the previously visited users of that location. Apart from the existing systems, our approach not only limited to users traveling interest but also recommends a travel sequence. Our sys-tem also suggest best visiting time, most suitable season, preference of visiting the nearby places and traveling route to reach to your desired location. Here the user can create his friend list and can share his experience of visit to his friends. This user given experience is taken as a feedback by the system to update his recommendations.
TripBuilder: A Tool for Recommending Sightseeing Tours
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We propose TripBuilder, an user-friendly and interactive system for planning a time-budgeted sightseeing tour of a city on the basis of the points of interest and the patterns of movements of tourists mined from user-contributed data. The knowledge needed to build the recommendation model is entirely extracted in an unsupervised way from two popular collaborative platforms: Wikipedia 1 and Flickr 2 . Trip-Builder interacts with the user by means of a friendly Web interface 3 that allows her to easily specify personal interests and time budget. The sightseeing tour proposed can be then explored and modified. We present the main components composing the system.