Paper presented in RecSys ACM conference 2013

1. Hybrid Event Recommendation
using
Linked Data and User Diversity
Houda Khrouf and Raphaël Troncy
{khrouf, troncy}@eurecom.fr
Eurecom, Sophia Antipolis, France
The 7th ACM Recommender Systems Conference
Oct 12-16, 2013 Hong Kong

2. Outline
 Event Recommendation
 Collaborative Filtering
 Content-based
 RDF Modeling and Similarity computation
 User Interest Detection
 Hybrid Approach
 Evaluation and Conclusion
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3. Events on the web
 Millions of active users
 Thousands of events per day
 Highly diverse content
Recommender Systems?
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4. What do users think?
Seen on Last.Fm
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5. Is this event interesting?
Decision
Time
Places
EVENTS
Decision factors (depends on type)
• Where is it? (Location)
• Who’s going? (Participants)
Attendees
Tags/Topics
• When is it? (Time)
• What is it? (Content)
• Who is involved? (players)
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Performers
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6. Collaborative Filtering (CF)
 Predict the event the user will attend
based on the attendance of other
like minded users
 Events are transient items
inducing a very sparse user
attendance matrix (sparsity 99%)
similar
 Best choice to reflect the social
dimension, but:
 Apart from the social information,
there is no explicit consideration
of the other factors
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7. Content-based Recommendation (CB)
 Recommend new events that match the user profile based on
their descriptions
Event context:
-
Location (geo-coordinates, city…)
-
Time
-
Topics/Tags
-
Performers (genres, tags…)
Events are entities with attributes and relational attributes (links)
to other entities
Events similarity depends on the similarity of related entities
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8. User Profile
 The user profile is based on past attended events
 Topical Diversity: real-world events range from large festivals to
small concerts and social gatherings
 A user might be interested in some specific topics/performers
during the event
We need to alleviate the profile
diversity and detect the user’s
interests
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9. Approach and Contributions
 Events similarity
 Structured RDF event model
 Similarity in Linked Data
 Data enrichment with DBpedia
 User interests detection using LDA (Latent Dirichlet
Allocation)
 Hybrid recommendation (CF+CB)
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10. LODE Ontology
LODE is a minimal model that encapsulates the factual properties of events: What,
Where, When and Who.
URL: http://linkedevents.org/ontology
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11. subjects
Linked Data in a Tensor Space
objects
For each property p, and for each object op [1]
𝑾𝑾 𝒐𝒐,𝒆𝒆 = 𝒇𝒇 𝒐𝒐,𝒆𝒆 ∗ 𝒍𝒍 𝒍𝒍 𝒍𝒍
𝒑𝒑
𝒑𝒑
|𝑬𝑬|
|𝑬𝑬 𝒐𝒐,𝒑𝒑 |
[1] T. Di Noia et al. Linked open data to support content-based recommender systems. In 8th
International Conference on Semantic Systems, Graz, Austria, 2012.
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12. Events Similarity
 Similarity according to one property p:
𝐜𝐜𝐜𝐜𝐜𝐜𝐜𝐜 𝐜𝐜 𝐩𝐩 𝐞𝐞 𝟏𝟏 , 𝐞𝐞 𝟐𝟐 =
∑ 𝒐𝒐∈𝑶𝑶
𝒑𝒑
∑ 𝒐𝒐∈𝑶𝑶 𝒘𝒘 𝒐𝒐,𝐞𝐞 𝟏𝟏
𝟐𝟐
𝒘𝒘 𝒐𝒐,𝐞𝐞 𝟏𝟏 ∗ 𝒘𝒘 𝒐𝒐,𝐞𝐞 𝟐𝟐
𝒑𝒑
∗
𝒑𝒑
𝒑𝒑
∑ 𝒐𝒐∈𝑶𝑶 𝒘𝒘 𝒐𝒐,𝐞𝐞 𝟐𝟐
𝟐𝟐
 Similarity between two events:
𝒔𝒔𝒔𝒔 𝒔𝒔 𝒆𝒆 𝟏𝟏 , 𝒆𝒆 𝟐𝟐 =
∑ 𝒑𝒑∈𝑷𝑷 𝜶𝜶 𝒑𝒑 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 𝒑𝒑 𝒆𝒆 𝟏𝟏 , 𝒆𝒆 𝟐𝟐
|𝑷𝑷|
Not adapted for discriminant properties associated with
highly sparse adjacency matrix
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13. Events Similarity

Discriminability
𝑫𝑫𝑫𝑫𝑫𝑫𝑫𝑫 𝒑𝒑 =

𝒐𝒐 𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕 = < 𝒔𝒔, 𝒑𝒑, 𝒐𝒐 > ∈ 𝑮𝑮 |
|𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕 = < 𝒔𝒔, 𝒑𝒑, 𝒐𝒐 > ∈ 𝑮𝑮|
Similarity-based Interpolation
𝑾𝑾
𝒑𝒑
𝒐𝒐 𝟐𝟐 ,𝒆𝒆 =
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|𝑬𝑬|
𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦(𝒐𝒐 𝟐𝟐 , 𝒐𝒐) ∗ 𝒍𝒍 𝒍𝒍 𝒍𝒍
𝒐𝒐∈𝑶𝑶 𝒑𝒑,𝒆𝒆
|𝑬𝑬 𝒐𝒐 𝟐𝟐 ,𝒑𝒑 |
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o1
p
similar
e
p
Interpolation of a
fictitious link
13
o2

14. Interest Detection
How to detect user interests from diverse event space?
Latent Dirichlet
Topic distribution over
each event (T=30)
Allocation (LDA)
Events
[Blei et al 2003]
Tags
Diversity
score
Attended
events Eu
User Interest
Mean of
the variances
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Distribution
Variance of each
topic
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15. Hybrid Recommendation

Content-based rank:
𝒓𝒓 𝒄𝒄𝒄𝒄++

Hybrid rank
∑ 𝒆𝒆 𝒊𝒊 ∈ 𝑬𝑬 𝒖𝒖 ∑ 𝒑𝒑∈ 𝑷𝑷 𝜶𝜶 𝒑𝒑 𝜷𝜷 𝒑𝒑 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 𝒑𝒑 (𝒆𝒆 𝒊𝒊 , 𝒆𝒆)
𝒖𝒖, 𝒆𝒆 =
𝑷𝑷 ∗ |𝑬𝑬 𝒖𝒖 |
𝒓𝒓 𝒖𝒖, 𝒆𝒆 = 𝒓𝒓 𝒄𝒄𝒄𝒄++ 𝒖𝒖, 𝒆𝒆 + 𝝀𝝀 𝒄𝒄𝒄𝒄 𝒓𝒓 𝒄𝒄𝒄𝒄 𝒖𝒖, 𝒆𝒆
CF rank: Common events
between u and RSVP users
αp = property weight
βp = interest weight
λ cf = CF weight
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16. Experiments
 Open RDF Dataset (EventMedia)
 Visualization: http://eventmedia.eurecom.fr
 SPARQL: http://eventmedia.eurecom.fr/sparql
 Learning the rank weights:
 Linear regression with gradient descent
 Genetic Algorithm
 Particle Swarm Optimization
 Evaluation: training 70% - test 30 %
 2.436 events in UK from Last.Fm , 481 active users, 14.748 artists,
897 locations (available on request)
 precision/recall of Top-N recommendations
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17. Sparsity Reduction
location
agent
subject
Without processing
0.9942
0.9174
0.3175
Similarity Interpolation
0.6854
0.7392
-
DBpedia enrichment
-
-
0.2843
Sparsity rates of adjacency matrices
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18. User Diversity
Score ≈ 1 => strong interest
Score ≈ 0 => cold-start users
Most of users have relatively high interests towards some topics
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19. Learning weights evaluation
PSO has better
performance
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20. CB+CF evaluation
𝛃𝛃 𝐢𝐢 𝐢𝐢 𝐢𝐢 𝐢𝐢 𝐢𝐢𝐢𝐢𝐢𝐢𝐢𝐢 >
𝟒𝟒 × 𝛃𝛃 𝐧𝐧𝐧𝐧−𝐢𝐢 𝐢𝐢 𝐢𝐢 𝐢𝐢 𝐢𝐢𝐢𝐢𝐢𝐢𝐢𝐢
Interest Detection
High influence of social
information in event
recommendation
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21. Comparison with other approaches
Probability based Extended Profile Filtering (UBExtended): T. D. Pessemie et al. Collaborative
recommendations with content-based filters for cultural activities via a scalable event distribution platform.
Multimedia.Tools Appl., 58(1):167-213, 2012.
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22. Conclusion
 Effectiveness of Semantic Web technologies to steer data retrieval
and processing
 Importance of the social information and the user interest model in
event recommendation
 Future work:
 Other features: popularity, temporal patterns, weather, etc…
 Test the system scalability on large datasets using spatial and/or
temporal indexing of user attendance
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