A Trajectory-Based Recommender System for Tourism

Baraglia, Ranieri; Frattari, Claudio; Muntean, Cristina Ioana; Nardini, Franco Maria; Silvestri, Fabrizio

doi:10.1007/978-3-642-35236-2_20

Cited by 11 publications

(15 citation statements)

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“…Trajectories are a concise representation of the behavior of moving objects as sequences of regions frequently visited with typical travel time. Trajectory-based models are exploited in [11], [3], [7] to predict the most likely locations that are of interest for a user.…”

Section: Related Workmentioning

confidence: 99%

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Baraglia

Muntean

Nardini

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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In this paper, we tackle the problem of predicting the "next" geographical position of a tourist given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68 dimension feature vector, specifically designed for tourism related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions.

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Section: Related Workmentioning

confidence: 99%

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Baraglia

Muntean

Nardini

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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“…In the last column of Table 3.1, we can see that, regardless the type of SN used in the reviewed projects, the collected data are quite similar: 87% of them use an SN for obtaining "Geotag Photos", that is, labels that contain the geographical identification metadata, such as latitude and longitude coordinates, though they can also include altitude, bearing and distance, accuracy data, like [200,186], among others; 71% extract "Geotag Labels", which are labels indicating the name of the city, country, address or labels that describe the photo, fundamental in projects such as [186,96]; and, finally, the "Geotag Timestamp", which indicates when a photo (for example) was taken, is used in 45% of the projects [197,201].…”

Section: What Data Are Extracted From Social Network?mentioning

confidence: 99%

“…Only 6% of papers used a "Geotag Weather", tags that contain weather information for a particular location, which helps the development of contextaware systems [97,194,200]; the same figure has the "Rating Items" [185,190], that means, the extraction of ratings such as online evaluations made by the users, which indicates their level of satisfaction (e.g. stars, ranking, likes) regarding restaurants, hotels, cities, POIs, routes, etc.…”

Section: What Data Are Extracted From Social Network?mentioning

confidence: 99%

“…For example, [199] proposed an algorithm for the interactive generation of personalised recommendations of POIs based on the knowledge mined from Flickr photos and Wikipedia. [200] created, on one hand, a knowledge model, used for calculating suggestions, and used, on the other hand, information of the path of a current user during a visit, that combined with the first one, allowed the system to produce a list of suggestions as possible locations to visit.…”

Section: What Recommendation Techniques Are Used?mentioning

confidence: 99%

“…For instance, [91] tried to improve both accuracy and confidence, to make satisfactory recommendations of georeferenced photos without prior knowledge of the user profile, considering only its current context; Also to analyse, the context in which the photos were taken is relevant in making recommendations; and the usage of a context model considering various contextual dimensions may lead to an improved recommendation comparing to the result of one which uses only one context attribute (e.g., location). Others combined accuracy with coverage [200,210,211], that is, even when an RSs is highly accurate, it may often not be able to ever recommend a certain proportion of the items, or it may not be able to ever recommend to a certain proportion of the users (this measure is referred to as coverage). Due to this limitation the trade-off between accuracy and coverage always needs to be incorporated into the evaluation process.…”

Section: What Properties Of Recommender Systems Are Used?mentioning

confidence: 99%

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Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks

Santos¹

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I n daily life, people usually rely on recommendations, traditionally given by other people (family, friends, etc.) for their most varied decisions. In the digital world, this is not different, given that recommender systems are present everywhere in such a way that we no longer realize. The main goal of these systems is to assist users in the decision-making process, generating recommendations that are of their interest and based on their tastes. These recommendations range from products in e-commerce websites, like books to read or places to visit to what to eat or how long one should walk a day to have a healthy life, who to date or who one should follow on social networks. And this is an increasing area. On the one hand, we have more and more users on the internet whose life is somewhat digitized, given than what one does in the "real world" is represented in a certain way in the "digital world". On the other hand, we suffer from information overload, which can be mitigated by the use of recommendation systems. However, these systems also face some problems, such as the cold start problem and their need to be more and more "human", "personalised" and "precise" in order to meet the yearning of users and companies. In this challenging scenario, personality-based recommender systems are being increasingly studied, since they are able to face these problems. Some recent projects have proposed the use of the human personality in recommenders, whether as a whole or individually by facet in order to meet those demands. Therefore, this thesis is devoted to this new area of personality-based recommendation, focusing on one of its most important traits, the curiosity. Additionally, in order to exploit the information already present on the internet, we will implicitly obtain information from social networks. Thus, this work aims to build a better experience for the end user through a new approach that offers an option for some of the gaps identified in personalitybased recommendation systems. Among these gap improvements, the use of social networks to feed the recommender systems soften the cold start problem

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LearningTour: A Machine Learning Approach for Tour Recommendation Based on Users’ Historical Travel Experience

Gao

et al. 2019

Database Systems for Advanced Applications

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A Trajectory-Based Recommender System for Tourism

Cited by 11 publications

References 11 publications

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Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks

LearningTour: A Machine Learning Approach for Tour Recommendation Based on Users’ Historical Travel Experience

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