Slides for our presentation at the ACM WebSci'17

1. Where Could We Go? Recommendations for Groups
in Location-Based Social Networks
F. Ayala-Gómez, B. Dároczy, M. Mathioudakis, A. Benczúr, A. Gionis
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA 1

2. Motivation
Location-
Based Social
Networks
(LBSNs) :
• Users share:
• Places they go
• People with whom they are
LBSN
Recommends
• Points of Interest (POI)
• Venues not visited yet
Existing work
• Recommendations for individual users
• Recommending POIs to groups is scarce
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA 2

3. Problem
Recommending a list of unvisited POIs to a group
of users in areas that the group frequents
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA 3

4. Research Questions
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
RQ1: How do groups behave in LBSNs?
RQ2: How do preferences change when users are
alone vs. when they are in a group?
RQ3: How to recommend items in the areas that a
group frequents?
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5. Public Check-in
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
Publicly
Shared
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6. Data Collection (Snowball sampling)
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA 6

7. F. Ayala-Gómez et. al. Where could we go? Recommendations for groups in
location-based social networks, ACM WebSci’17. Troy, NY, USA. [1]
5.6M individual check-ins and 1M group check-ins
140K users, 500K venues, 780 categories, 450K groups
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA 7

8. WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA 8
Top 3 cities after cleaning dataset

9. Groups move less
than users and
their check-ins are
less frequent.
Groups in LBSNs
are small.
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
RQ 1: Group Behavior
User Check-ins Group Check-ins
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10. Groups prefer other areas
than their members.
Groups
Check-ins
User
Check-ins
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
RQ 2: Individual vs. Group Preferences
DBSCAN clusters centroids
Weighted Average of movement for user to the groups
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11. Groups prefer other
types of venues than
their members.
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
RQ 2: Individual vs. Group Preferences
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12. Proposed
Model:
Geo-Group-
Recommender
(GGR)
• Geography KDE
• Category and
geography feature
engineering
• Recommender
system
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
RQ3: Group Recommendations
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13. Random split per group and cluster
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA 13

14. WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
RQ3: Group Recommendations
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15. Recommender
Systems using
Groups profiles
works better than
averaging individual
recommendations.
Average Individual Ratings (AIR)
Average Without Misery (AWM)
Average Least Misery (ALM)
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
RQ3: Group Recommendations
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16. Geo-Group-Recommender (GGR)
improves the performance
compared to the baselines.
RQ3: Group Recommendations
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
Mexico City Recall@K
IALS
KDE ∩ SGD CAT
GGR Models
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17. Izmir Recall@K
KDE ∩ SGD GEO
IALSGGR Models
RQ3: Group Recommendations
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
Istanbul Recall@K
KDE ∩ IALS
IALS*
IALS*: Not a GGR recommender system
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18. Empirical findings on groups behavior and their preferences in LBSN.
Our proposed class of hybrid recommender systems: Geo-Group-Recommender
outperforms its baselines.
Future work:
•Understand the reasons why the models perform different for different cities
•Try more sophisticated ways to combine user preferences
•Experiment with more cities
•Add Cat ang Geo to IALS
Code and Data available for research
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA
Conclusions
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19. Thanks!
Where Could We Go? Recommendations for Groups in
Location-Based Social Networks
F. Ayala-Gómez, B. Dároczy, M. Mathioudakis, A. Benczúr, A. Gionis
WEBSCI ’17, JUNE 25-28, 2017, TROY, NY, USA 19