Top-k context-aware tour recommendations for groups

Frederick Ayala-Gómez*, Barış Keniş, Pınar Karagöz, András Benczúr

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


Cities offer a large variety of Points of Interest (POI) for leisure, tourism, culture, and entertainment. This offering is exciting and challenging, as it requires people to search for POIs that satisfy their preferences and needs. Finding such places gets tricky as people gather in groups to visit the POIs (e.g., friends, family). Moreover, a group might be interested in visiting more than one place during their gathering (e.g., restaurant, historical site, coffee shop). This task is known to be the orienteering under several constraints (e.g., time, distance, type ordering). Intuitively, the POI preference depends on the group, and on the context (e.g., time of arrival, previously visited POIs in the itinerary). Recent solutions to the problem focus on recommending a single itinerary, aggregating individual preferences to build the group preference, and contextual information does not affect the scheduling process. In this paper, we present a novel approach to the following setting: Given a history of previous group check-ins, a starting POI, and a time budget, find top-k sequences of POIs relevant to the group and context that satisfy the constraints. Our proposed solution consists of two primary steps: training a POI recommender system for groups, and solving the orienteering problem on a candidate set of POIs using Monte Carlo Tree Search. We collected a ground-truth dataset from Foursquare, and show that the proposed approach improves the performance in comparison to a Greedy baseline technique.

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence - 17th Mexican International Conference on Artificial Intelligence, MICAI 2018, Proceedings
EditorsIldar Batyrshin, María de Lourdes Martínez-Villaseñor, Hiram Eredín Ponce Espinosa
Number of pages18
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventMexican International Conference on Artificial Intelligence - Guadalajara, Mexico
Duration: 22 Oct 201827 Oct 2018
Conference number: 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11289 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceMexican International Conference on Artificial Intelligence
Abbreviated titleMICAI


  • Orienteering
  • Recommender systems for groups
  • Tour recommendation

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