Hybrid Event Recommendation
using
Linked Data and User Diversity
Houda Khrouf and Raphaël Troncy
{khrouf, troncy}@eurecom....
Outline
 Event Recommendation
 Collaborative Filtering
 Content-based

 RDF Modeling and Similarity computation
 User...
Events on the web

 Millions of active users
 Thousands of events per day
 Highly diverse content
Recommender Systems?
...
What do users think?
Seen on Last.Fm

10/15/2013

7th ACM Recommender Systems 2013, Hong Kong

4
Is this event interesting?

Decision

Time

Places

EVENTS

Decision factors (depends on type)
• Where is it? (Location)
•...
Collaborative Filtering (CF)
 Predict the event the user will attend
based on the attendance of other
like minded users

...
Content-based Recommendation (CB)
 Recommend new events that match the user profile based on
their descriptions
Event con...
User Profile
 The user profile is based on past attended events
 Topical Diversity: real-world events range from large f...
Approach and Contributions

 Events similarity
 Structured RDF event model
 Similarity in Linked Data
 Data enrichment...
LODE Ontology
LODE is a minimal model that encapsulates the factual properties of events: What,
Where, When and Who.
URL: ...
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...
Events Similarity
 Similarity according to one property p:
𝐜𝐜𝐜𝐜𝐜𝐜𝐜𝐜 𝐜𝐜 𝐩𝐩 𝐞𝐞 𝟏𝟏 , 𝐞𝐞 𝟐𝟐 =

∑ 𝒐𝒐∈𝑶𝑶

𝒑𝒑
∑ 𝒐𝒐∈𝑶𝑶 𝒘𝒘 𝒐𝒐,𝐞𝐞 𝟏...
Events Similarity


Discriminability
𝑫𝑫𝑫𝑫𝑫𝑫𝑫𝑫 𝒑𝒑 =



𝒐𝒐 𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕 = < 𝒔𝒔, 𝒑𝒑, 𝒐𝒐 > ∈ 𝑮𝑮 |
|𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕 = < 𝒔𝒔, 𝒑𝒑, ...
Interest Detection
How to detect user interests from diverse event space?
Latent Dirichlet
Topic distribution over
each ev...
Hybrid Recommendation


Content-based rank:
𝒓𝒓 𝒄𝒄𝒄𝒄++



Hybrid rank

∑ 𝒆𝒆 𝒊𝒊 ∈ 𝑬𝑬 𝒖𝒖 ∑ 𝒑𝒑∈ 𝑷𝑷 𝜶𝜶 𝒑𝒑 𝜷𝜷 𝒑𝒑 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 𝒑𝒑...
Experiments
 Open RDF Dataset (EventMedia)
 Visualization: http://eventmedia.eurecom.fr
 SPARQL: http://eventmedia.eure...
Sparsity Reduction

location

agent

subject

Without processing

0.9942

0.9174

0.3175

Similarity Interpolation

0.6854...
User Diversity

Score ≈ 1 => strong interest
Score ≈ 0 => cold-start users

Most of users have relatively high interests t...
Learning weights evaluation

PSO has better
performance

10/15/2013

7th ACM Recommender Systems 2013, Hong Kong

19
CB+CF evaluation
𝛃𝛃 𝐢𝐢 𝐢𝐢 𝐢𝐢 𝐢𝐢 𝐢𝐢𝐢𝐢𝐢𝐢𝐢𝐢 >
𝟒𝟒 × 𝛃𝛃 𝐧𝐧𝐧𝐧−𝐢𝐢 𝐢𝐢 𝐢𝐢 𝐢𝐢 𝐢𝐢𝐢𝐢𝐢𝐢𝐢𝐢

Interest Detection

High influence of social...
Comparison with other approaches

Probability based Extended Profile Filtering (UBExtended): T. D. Pessemie et al. Collabo...
Conclusion

 Effectiveness of Semantic Web technologies to steer data retrieval
and processing
 Importance of the social...
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Hybrid Event Recommendation using Linked Data and User Diversity

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Hybrid Event Recommendation using Linked Data and User Diversity

  1. 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. 2. Outline  Event Recommendation  Collaborative Filtering  Content-based  RDF Modeling and Similarity computation  User Interest Detection  Hybrid Approach  Evaluation and Conclusion 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 2
  3. 3. Events on the web  Millions of active users  Thousands of events per day  Highly diverse content Recommender Systems? 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 3
  4. 4. What do users think? Seen on Last.Fm 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 4
  5. 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) 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong Performers 5
  6. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 6
  7. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 7
  8. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 8
  9. 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) 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 9
  10. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 10
  11. 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. 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 11
  12. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 12
  13. 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 = 10/15/2013 |𝑬𝑬| 𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦𝐦(𝒐𝒐 𝟐𝟐 , 𝒐𝒐) ∗ 𝒍𝒍 𝒍𝒍 𝒍𝒍 𝒐𝒐∈𝑶𝑶 𝒑𝒑,𝒆𝒆 |𝑬𝑬 𝒐𝒐 𝟐𝟐 ,𝒑𝒑 | 7th ACM Recommender Systems 2013, Hong Kong o1 p similar e p Interpolation of a fictitious link 13 o2
  14. 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 10/15/2013 Distribution Variance of each topic 7th ACM Recommender Systems 2013, Hong Kong 14
  15. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 15
  16. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 16
  17. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 17
  18. 18. User Diversity Score ≈ 1 => strong interest Score ≈ 0 => cold-start users Most of users have relatively high interests towards some topics 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 18
  19. 19. Learning weights evaluation PSO has better performance 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 19
  20. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 20
  21. 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. 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 21
  22. 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 10/15/2013 7th ACM Recommender Systems 2013, Hong Kong 22

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