How Random Walks Can Help Tourism

Lucchese, Claudio; Perego, Raffaele; Silvestri, Fabrizio; Vahabi, Hossein; Venturini, Rossano

doi:10.1007/978-3-642-28997-2_17

Cited by 40 publications

(41 citation statements)

References 14 publications

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“…Other interesting papers in the domain of itinerary planning include [34], where the authors use GPS data to build an itinerary recommendation engine and evaluate it using Beijing as an example, [26], which focuses on suggesting routes in a city that also offer some utility to the user as opposed to just being the shortest sourcedestination paths, as well as various approaches that use geo-tagged social media, e.g., [4,21,24,25,30,32,33,35], and approaches based on personalization [5, 11, 12, 18-20, 22, 23, 27, 28]. A final interesting piece of related work is [31] where the authors study the orienteering problem in a tourist application from a game-theoretic view-point.…”

Section: Related Workmentioning

confidence: 99%

Orienteering Algorithms for Generating Travel Itineraries

Friggstad

Gollapudi

Kollias

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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We study the problem of automatically and efficiently generating itineraries for users who are on vacation. We focus on the common case, wherein the trip duration is more than a single day. Previous efficient algorithms based on greedy heuristics suffer from two problems. First, the itineraries are often unbalanced, with excellent days visiting top attractions followed by days of exclusively lower-quality alternatives. Second, the trips often re-visit neighborhoods repeatedly in order to cover increasingly low-tier points of interest. Our primary technical contribution is an algorithm that addresses both these problems by maximizing the quality of the worst day. We give theoretical results showing that this algorithm's competitive factor is within a factor two of the guarantee of the best available algorithm for a single day, across many variations of the problem. We also give detailed empirical evaluations using two distinct datasets: (a) anonymized Google historical visit data and (b) Foursquare public check-in data. We show first that the overall utility of our itineraries is almost identical to that of algorithms specifically designed to maximize total utility, while the utility of the worst day of our itineraries is roughly twice that obtained from other approaches. We then turn to evaluation based on human raters who score our itineraries only slightly below the itineraries created by human travel experts with deep knowledge of the area.

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Section: Related Workmentioning

confidence: 99%

Orienteering Algorithms for Generating Travel Itineraries

Friggstad

Gollapudi

Kollias

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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“…RWR-based recommendation systems have been shown to work effectively in many domains ( [51], [52], [53], [54], [55], [56], [57], [58], [59]), for which Bayesian transition matrices can also be introduced. …”

Section: Resultsmentioning

confidence: 99%

Random Walk-based Recommendation with Restart using Social Information and Bayesian Transition Matrices

Pongnumkul¹,

Motohashi²

2015

IJCA

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A recommendation system is an information retrieval system that employs user, product, and other related information to infer relationships among data to offer product recommendations. The basic assumption is that friends or users with similar behavior will have similar interests. The large number of products available today makes it impossible for any user to explore all of them and increases the importance of recommendation systems. However, a recommendation system normally requires comprehensive data relating users and products. Insufficiently comprehensive data creates difficulties for creating good recommendations. Recommendation systems for incomplete data have become an active research area. One approach to solve this problem is to use random walk with restart (RWR), which significantly reduces the quantity of data required and has been shown to outperform collaborative filtering, the currently popular approach. This study explores how to increase the efficiency of the RWR approach. We replace transition matrices that use information regarding relationships between user, usage, and tags with transition matrices that use Bayesian probabilities, and we compare the efficiency of the two approaches using mean average precision. An experiment was conducted using music information data from last.fm. The result shows that our approach provides better recommendations.

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“…Egyes esetekben lehetőség nyílik akár új barátokat vagy utitársakat szerezni, és javasolják hasonló ízlésvilágú emberek együtt utazását, példa erre Zheng és Xie (2011) alkalmazása. Lucchese et al (2012) az alapján készítenek turisták számára útvonaltervezése során helyszínekre vonatkozó ajánlásokat, hogy az adott városban a Flickr-en vagy más közösségi oldalon található fotók milyen gyakorisággal készülnek egy adott helyszínről. Ezt kiegészítve a helyszín Wikipedia oldaláról nyerhető információk-kal, könnyen pontozhatók fontosságuk szerint a helyszí-nek.…”

Section: Turisztikai Ajánlórendszerekunclassified

Turistatípusok azonosítása. Egy lehetséges turisztikai ajánlórendszer

Apáthy¹

2017

Veztud

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How Random Walks Can Help Tourism

Cited by 40 publications

References 14 publications

Orienteering Algorithms for Generating Travel Itineraries

Orienteering Algorithms for Generating Travel Itineraries

Random Walk-based Recommendation with Restart using Social Information and Bayesian Transition Matrices

Turistatípusok azonosítása. Egy lehetséges turisztikai ajánlórendszer

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