Presentation of the paper Event Recommendation in Event-based Social Networks at the 1st International Workshop on Social Personalisation (SP 2014) co-located with the 25th ACM Conference on Hypertext and Social Media

1. Event Recommendation in Event-Based Social
Networks
Augusto Queiroz and Leandro Balby Marinho
Information Systems and Database Group
Federal University of Campina Grande (UFCG)
1st International Workshop on Social Personalisation (SP 2014)
Co-located with the 25th ACM Conference on Hypertext and Social Media
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2. Event-Based Social Networks (EBSN)
People can create events of any kind and share it with other users.
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3. Recommending in EBSN
I Problem: Among the large number of events available in
EBSNs, which ones best match the user's preferences?
I More challenging than traditional domains (why?)
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4. Movie Recommendation: Collaborative Filtering
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5. Events to Recommend are Always in the Future
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6. Idea: Use RSVP as a Proxy
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7. Research Questions
I How sparse is the RSVP data and how it aects
collaborative-

8. ltering algorithms?
I In which point of the event life time users tend to provide
RSVPs?
I How the geographic distance between the users home and
active events aect their decision on attending these events?
I How simple and state-of-the-art algorithms compare in this
domain?
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9. Related Work
I [Liu et al. KDD'12]: Recommendation of users to events in
Meetup.
I [Khrouf et al. RecSys'13]: Recommendation of events in
Last.fm.
I Restricted domain: music concerts and festivals.
I Use of linked-open data on the domain of interest.
I Our Work:
I Recommendation of generic events.
I Experiments under the true level of sparsity found on EBSN.
I Investigation of previously unexplored features of EBSN.
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10. Data Collection
I Data collected from Meetup.com Data from January, 2010 to
December, 2011
I Cities Collected: Phoenix, Chicago and San Jose
City #Users #Events #RSVPs Sparsity
Phoenix 589 K 215 K 1.5 M 99.998%
Chicago 719 K 220 K 1,3 M 99.999%
San Jose 281 K 242 K 1.7 M 99.997%
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11. RSVP Analysis
45% of the
events have at
most 1 RSVP
90% of the
events have at
most 10 RSVPs
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12. Event Lifespan
80% of the
events have a
life time of at
most 100 days
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13. When do RSVPs Occur?
The more Yes
RSVPs, the
later it will be
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14. When do RSVPs Occur?
75% of the
RSVPs are
received during
the last 20% of
event life time.
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15. Distance Distribution
50% of the
RSVPs are to
events within
10 Km of users
home
95% of the
RSVPs are to
events within
100 Km.
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16. Data Preparation
Timed split: 6 time stamps, equally spaced in 6 months.
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17. Compared Algorithms
I Random
I Most-Popular
I Location-Aware
I BPR-MF
I User-KNN and Item-KNN
I Logistic-Regression: hybrid will all above (except random)
I Evaluation Metric: NDCG@20
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18. Results
KNNs have the
power.
Location-aware
as an alterantive
for full-cold
start.
NDCG@20 0.3
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19. Sparsity Analysis of the Test Set
Majority of
events in Test
have no Yes
RSVP in Train!
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20. NDCG@20 per Sparsity Level
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21. Conclusions and Future Works
I The largest majority of events are cold-start.
I RSVPs tend to be given close to the occurrence of the event.
I Despite the high sparsity of RSVP data, KNN-based
algorithms appear as the best single alternative.
I Matrix-factorization does not perform as well in this domain
as it does in other more typical domains.
I For future work: use categories, description and social
networks.
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22. References
Event-based social networks: linking the online and oine
social worlds. Proceedings of the 18th ACM SIGKDD
international conference on Knowledge discovery and data
mining, 2012.
Hybrid Event Recommendation Using Linked Data and User
Diversity. Proceedings of the 7th ACM Conference on
Recommender Systems, 2013.
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