This document proposes an approach for top-k context-aware tour recommendations for groups. It collects check-in data from location-based social networks to determine group preferences and contexts. Various recommender systems are trained on this group history data and evaluated. An Monte Carlo tree search approach is used to construct itineraries that maximize recommendation scores and diversity, incorporating contextual factors like popularity. Evaluation finds this context-aware approach performs better than baselines at recommending personalized group tours. Future work is needed to better understand system characteristics and improve candidate selection and scoring.

1. Top-k Context-Aware Tour Recommendations
for Groups
Frederick Ayala-Gómez, Barış Keniş, Pınar Karagöz, and András
Benczúr
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 1

2. Motivation
Cities • Large variety of POI
Groups
• Finding POI relevant for a group of users is
challenging and time consuming
Location-Based
Social Networks
(LBSNs) :
• Users share:
• Places they go
• People with whom they are
LBSN
Recommends
• Points of Interest (POI)
Existing work
• POIs recommendation for groups
• Itinerary for groups
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 2

3. Problem
Recommending
top-k sequences
of POIs for a
known group,
given a starting
POI, time
constraint, and
contextual
information (e.g.,
popularity of the
venue at time of
arrival)
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 3

4. Research Questions
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO
How to use group recommendations for
the POI itinerary construction?
How to combine context in the itinerary
recommendation?
How to recommend top-k itineraries?
4

5. MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 5
Overview of the proposed approach

6. Ground Truth Dataset- Public Check-in
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO
Publicly
Shared
6
Ayala-Gómez, Frederick, et al. "Where Could We Go?: Recommendations for Groups in Location-Based Social Networks." WebSci’17

7. Data Collection (Snowball sampling)
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 7
Ayala-Gómez, Frederick, et al. "Where Could We Go?: Recommendations for Groups in Location-Based Social Networks." WebSci’17

8. MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 8
Top cities after cleaning dataset

9. MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 9
Feature Engineering
Content
features
Tips, phrases, attributes
tf-idf
Transition
Features:
Pairwise transitions probabilities between
POI transitions
Temporal
features:
Popularity of POI at different day times (i.e.,
05-08, 09-11, 12-14, 15-17, 17-19, 21-04)

10. MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 10
Recommender System Training
Recommender
Systems Content-based
Item-based
iALS
Recommendations
for groups Group profile
Aggregation of individual recommendations

11. MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO
Recommender System Training
11
NDCG@100

12. Itinerary Planning
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 12
Input Group profile
Starting POI
A time budget
Best recommender system for the city

13. Candidate Set
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 13
For a given starting POI we consider k number of nearest neighbors.

14. Top-k itinerary construction using MCTS
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 14
Upper
confidence
Bound for
Trees (UCT)
Iteratively builds by expanding nodes in each iteration
Selecting the most promising node
Estimating the expected reward of the selected node
Back-propagating the reward to the parents
Tree policy Expands a node if it is not fully expanded or terminal, otherwise it picks the
best child.
Default
Policy
Simulates an expected reward, if the node is non-terminal.
Backup Updating the parents of the selected best-child according to the expected
reward.

15. Top-k itinerary construction using MCTS
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 15
MCTS_Simple Selects uniformly random the POIs
Expected reward is
MCTS_Score Replaces uniformly random sampling with greedy search
The expected reward is the same as in MCTS_Simple
MCTS Context Replaces uniformly random sampling with greedy search
Use the score of the recommender system, and contextual information
Recommendation score
Number of occurrence of venue
type in itinerary
Diversity parameter > 1

16. Mean value of the itineraries. The value of an itinerary
is the sum of the pois recommendations.
Mean precision of the top 10 itineraries
recommendation.
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 16
Top-k itinerary construction using MCTS
Mean recall of the top 10 itineraries
recommendation.

17. Findings
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 17
Finding 1: LBSNs provide enough clues to determine group preferences for venues and contextual
information.
Finding 2: Models constructed with group history perform better than the models includingthe aggregation
of individual recommendations.
Finding 3: Predicting the next POI using a fixed category type improves the quality of the recommender
systems considerably.
Finding 4: Summation of individual recommendation scores provide the highest performance.
Finding 5: Collaborative filtering with implicit feedback (under group) had the highest performance (except
for Toyko)
Finding 6: In itinerary construction, the MCTS methods outperform the greedy baseline in most of the
cases in terms of POI scoring, precision and recall.
Finding 7: MCTS Context performed similarly or better than MCTS Simple, and MCTS Score.
Finding 8: It is hard to conclude about the characteristics of the constructed itineraries.

18. We present an approach for solving the top-k context-aware tour
recommendations for groups problem by using MCTS and contextual
information
Future work:
• Understanding what makes different recommender systems to perform better
• Using category type as constraint during itinerary generation
• Experimenting with different candidate selection strategies
• Use more advanced scoring functions that combine contextual information with
recommendations.
• Cold start
MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO
Conclusions
18

19. MICAI 2018, OCTOBER 22-27, 2018, GUADALAJARA, MÉXICO 19
Thanks!
Questions?
Top-k Context-Aware Tour Recommendations
for Groups
Frederick Ayala-Gómez, Barış Keniş, Pınar Karagöz, and András
Benczúr