66 research outputs found
Smart Trip Alternatives for the Curious
International audienceWhen searching for flights, current systems often suggest routesinvolving waiting times at stopovers. There might exist alternative routes which aremore attractive from a touristic perspective because their duration isnot necessarily much longer while offering enough time in anappropriate place. Choosing among suchalternatives requires additional planning efforts to make sure thate.g. points of interest can conveniently be reached in theallowed time frame. We present a system that automatically computes smart tripalternatives between any two cities. To do so, it searchespoints of interest in large semantic datasets considering theset of accessible areas around each possible layover. It then elects feasible alternatives and displays theirdifferences with respect to the default trip
The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City
When providing directions to a place, web and mobile mapping services are all
able to suggest the shortest route. The goal of this work is to automatically
suggest routes that are not only short but also emotionally pleasant. To
quantify the extent to which urban locations are pleasant, we use data from a
crowd-sourcing platform that shows two street scenes in London (out of
hundreds), and a user votes on which one looks more beautiful, quiet, and
happy. We consider votes from more than 3.3K individuals and translate them
into quantitative measures of location perceptions. We arrange those locations
into a graph upon which we learn pleasant routes. Based on a quantitative
validation, we find that, compared to the shortest routes, the recommended ones
add just a few extra walking minutes and are indeed perceived to be more
beautiful, quiet, and happy. To test the generality of our approach, we
consider Flickr metadata of more than 3.7M pictures in London and 1.3M in
Boston, compute proxies for the crowdsourced beauty dimension (the one for
which we have collected the most votes), and evaluate those proxies with 30
participants in London and 54 in Boston. These participants have not only rated
our recommendations but have also carefully motivated their choices, providing
insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201
Tour recommendation for groups
Consider a group of people who are visiting a major touristic city, such as NY, Paris, or Rome. It is reasonable to assume that each member of the group has his or her own interests or preferences about places to visit, which in general may differ from those of other members. Still, people almost always want to hang out together and so the following question naturally arises: What is the best tour that the group could perform together in the city? This problem underpins several challenges, ranging from understanding people’s expected attitudes towards potential points of interest, to modeling and providing good and viable solutions. Formulating this problem is challenging because of multiple competing objectives. For example, making the entire group as happy as possible in general conflicts with the objective that no member becomes disappointed. In this paper, we address the algorithmic implications of the above problem, by providing various formulations that take into account the overall group as well as the individual satisfaction and the length of the tour. We then study the computational complexity of these formulations, we provide effective and efficient practical algorithms, and, finally, we evaluate them on datasets constructed from real city data
Learning Points and Routes to Recommend Trajectories
The problem of recommending tours to travellers is an important and broadly
studied area. Suggested solutions include various approaches of
points-of-interest (POI) recommendation and route planning. We consider the
task of recommending a sequence of POIs, that simultaneously uses information
about POIs and routes. Our approach unifies the treatment of various sources of
information by representing them as features in machine learning algorithms,
enabling us to learn from past behaviour. Information about POIs are used to
learn a POI ranking model that accounts for the start and end points of tours.
Data about previous trajectories are used for learning transition patterns
between POIs that enable us to recommend probable routes. In addition, a
probabilistic model is proposed to combine the results of POI ranking and the
POI to POI transitions. We propose a new F score on pairs of POIs that
capture the order of visits. Empirical results show that our approach improves
on recent methods, and demonstrate that combining points and routes enables
better trajectory recommendations
Sieving tourism destinations: Decision-making processes and destination choice implications
Purpose: To introduce and define the concept of sieving tourism destinations as an umbrella term representing faster decision-making processes compared to destination choice models, and to demonstrate its usefulness for both sides of consumption and production of tourism attractions.
Methods: Fast decision at the consumers’ demand side is demonstrated via an exploratory graphic model. Producers’ supply side sieving is measured by observing data elimination on two public serving internet platforms compared to a baseline taken from special interest group tour operators representing Jewish heritage attractions in Sicily and Thessaloniki.
Results: On the demand side, nowadays market conditions enable destination choice decision making in a few simple steps often interpreted as spontaneous, intuitive, or irrational. Quantitative analyses on the supply side provided measurable sieving ratios. They reveal careful partial sieving performed at local level editorship, while much harsher sieving occurs on social media platforms. This is interpreted as a market failure related to niche and special interest groups attractions.
Implications: The demand side findings call for targeted marketing distinguishing customers not only by income but also by temperament, mood, and personality. The supply side findings call for careful examination of the conditions for inclusion and exclusion from the list of attractions as well as the need to remedy the concealment of minor attractions from social media platforms
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