How long to stay where? On the amount of item consumption in travel recommendation

Linus W. Dietz; Wolfgang Wörndl

How long to stay where? On the amount of item consumption in travel recommendation

Linus W. Dietz
, Wolfgang Wörndl

Informatics 11 - Chair of Connected Mobility

Technical University of Munich

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

Recommender systems could benefit from not only recommending the most fitting items, but also in what quantity the user should consume them. For example, a personalized travel recommender system could indicate not just which city one should travel to, but also how much time to spend there. We present a data-driven solution to this problem based on mining trips from location-based social networks. To determine the recommended duration of stay at a destination, we consider how long travelers typically stay at different cities and how much time the current user generally spends visiting cities.

Original language	English
Pages (from-to)	31-35
Number of pages	5
Journal	CEUR Workshop Proceedings
Volume	2431
State	Published - 2019
Event	2019 ACM Conference on Recommender Systems Late-breaking Results, ACM RecSys LBR 2019 co-located with the 13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark Duration: 16 Sep 2019 → 20 Sep 2019

Keywords

Recommender systems
Travel recommendation
User modeling

Cite this

@article{4d20680af57544588527bdff809882a2,

title = "How long to stay where? On the amount of item consumption in travel recommendation",

abstract = "Recommender systems could benefit from not only recommending the most fitting items, but also in what quantity the user should consume them. For example, a personalized travel recommender system could indicate not just which city one should travel to, but also how much time to spend there. We present a data-driven solution to this problem based on mining trips from location-based social networks. To determine the recommended duration of stay at a destination, we consider how long travelers typically stay at different cities and how much time the current user generally spends visiting cities.",

keywords = "Recommender systems, Travel recommendation, User modeling",

author = "Dietz, \{Linus W.\} and Wolfgang W{\"o}rndl",

note = "Publisher Copyright: Copyright {\textcopyright}2019 for this paper by its authors.; 2019 ACM Conference on Recommender Systems Late-breaking Results, ACM RecSys LBR 2019 co-located with the 13th ACM Conference on Recommender Systems, RecSys 2019 ; Conference date: 16-09-2019 Through 20-09-2019",

year = "2019",

language = "English",

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journal = "CEUR Workshop Proceedings",

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TY - JOUR

T1 - How long to stay where? On the amount of item consumption in travel recommendation

AU - Dietz, Linus W.

AU - Wörndl, Wolfgang

PY - 2019

Y1 - 2019

N2 - Recommender systems could benefit from not only recommending the most fitting items, but also in what quantity the user should consume them. For example, a personalized travel recommender system could indicate not just which city one should travel to, but also how much time to spend there. We present a data-driven solution to this problem based on mining trips from location-based social networks. To determine the recommended duration of stay at a destination, we consider how long travelers typically stay at different cities and how much time the current user generally spends visiting cities.

AB - Recommender systems could benefit from not only recommending the most fitting items, but also in what quantity the user should consume them. For example, a personalized travel recommender system could indicate not just which city one should travel to, but also how much time to spend there. We present a data-driven solution to this problem based on mining trips from location-based social networks. To determine the recommended duration of stay at a destination, we consider how long travelers typically stay at different cities and how much time the current user generally spends visiting cities.

KW - Recommender systems

KW - Travel recommendation

KW - User modeling

UR - https://www.scopus.com/pages/publications/85071776391

M3 - Conference article

AN - SCOPUS:85071776391

SN - 1613-0073

VL - 2431

SP - 31

EP - 35

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 2019 ACM Conference on Recommender Systems Late-breaking Results, ACM RecSys LBR 2019 co-located with the 13th ACM Conference on Recommender Systems, RecSys 2019

Y2 - 16 September 2019 through 20 September 2019

ER -

How long to stay where? On the amount of item consumption in travel recommendation

Abstract

Keywords

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