Event Recommendation in Event-Based SocialNetworks

Augusto Queiroz and Leandro Balby Marinho

Information Systems and Database GroupFederal 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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Event-Based Social Networks (EBSN)

People can create events of any kind and share it with other users.

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Recommending in EBSN

I Problem: Among the large number of events available inEBSNs, which ones best match the user’s preferences?

I More challenging than traditional domains (why?)

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Movie Recommendation: Collaborative Filtering

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Events to Recommend are Always in the Future

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Idea: Use RSVP as a Proxy

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Research Questions

I How sparse is the RSVP data and how it affectscollaborative-filtering algorithms?

I In which point of the event life time users tend to provideRSVPs?

I How the geographic distance between the users home andactive events affect their decision on attending these events?

I How simple and state-of-the-art algorithms compare in thisdomain?

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Related Work

I [Liu et al. KDD’12]: Recommendation of users to events inMeetup.

I [Khrouf et al. RecSys’13]: Recommendation of events inLast.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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Data Collection

I Data collected from Meetup.com Data from January, 2010 toDecember, 2011

I Cities Collected: Phoenix, Chicago and San Jose

City #Users #Events #RSVPs SparsityPhoenix 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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RSVP Analysis

> 45% of theevents have atmost 1 RSVP

≈ 90% of the

events have at

most 10 RSVPs

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Event Lifespan

≈ 80% of the

events have a

life time of at

most 100 days

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When do RSVPs Occur?

The more YesRSVPs, thelater it will be

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When do RSVPs Occur?

≈ 75% of theRSVPs arereceived duringthe last 20% ofevent life time.

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Distance Distribution

≈ 50% of theRSVPs are toevents within10 Km of usershome

≈ 95% of theRSVPs are toevents within100 Km.

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Data Preparation

Timed split: 6 time stamps, equally spaced in 6 months.

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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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Results

KNNs have thepower.

Location-awareas an alterantivefor full-coldstart.

NDCG@20 < 0.3

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Sparsity Analysis of the Test Set

Majority ofevents in Testhave no YesRSVP in Train!

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NDCG@20 per Sparsity Level

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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-basedalgorithms appear as the best single alternative.

I Matrix-factorization does not perform as well in this domainas it does in other more typical domains.

I For future work: use categories, description and socialnetworks.

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References

Event-based social networks: linking the online and offlinesocial worlds. Proceedings of the 18th ACM SIGKDDinternational conference on Knowledge discovery and datamining, 2012.

Hybrid Event Recommendation Using Linked Data and UserDiversity. Proceedings of the 7th ACM Conference onRecommender Systems, 2013.

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