hybrid event recommendation using linked data and user diversity
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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
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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
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Millions of active users
Thousands of events per day
Highly diverse content
Recommender Systems?
7th ACM Recommender Systems 2013, Hong Kong
What do users think?
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Seen on Last.Fm
EVENTS
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Is this event interesting?
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Attendees
Places Time
Tags/Topics
Performers
Decision
Decision factors (depends on type) • Where is it? (Location)
• Who’s going? (Participants)
• When is it? (Time)
• What is it? (Content)
• Who is involved? (players)
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Collaborative Filtering (CF)
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Predict the event the user will attend
based on the attendance of other like minded users
Best choice to reflect the social dimension, but:
Events are transient items
inducing a very sparse user attendance matrix (sparsity 99%)
Apart from the social information, there is no explicit consideration of the other factors
sim
ilar
Events are entities with attributes and relational attributes (links) to other entities
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Content-based Recommendation (CB)
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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 similarity depends on the similarity of related entities
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User Profile
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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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Approach and Contributions
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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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LODE Ontology
LODE is a minimal model that encapsulates the factual properties of events: What,
Where, When and Who. URL: http://linkedevents.org/ontology
Linked Data in a Tensor Space
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For each property p, and for each object op [1] 𝑾𝑾 𝒐𝒐,𝒆𝒆 𝒑𝒑 = 𝒇𝒇 𝒐𝒐,𝒆𝒆
𝒑𝒑 ∗ 𝒍𝒍𝒐𝒐𝒍𝒍|𝑬𝑬|
|𝑬𝑬𝒐𝒐,𝒑𝒑|
subj
ects
objects
[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.
Events Similarity
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Similarity between two events:
Similarity according to one property p:
Not adapted for discriminant properties associated with highly sparse adjacency matrix
𝐜𝐜𝐜𝐜𝐜𝐜𝐜𝐜𝐜𝐜𝐩𝐩 𝐞𝐞𝟏𝟏, 𝐞𝐞𝟐𝟐 = ∑ 𝒘𝒘𝒐𝒐,𝐞𝐞𝟏𝟏
𝒑𝒑 ∗ 𝒘𝒘𝒐𝒐,𝐞𝐞𝟐𝟐𝒑𝒑 𝒐𝒐∈𝑶𝑶
∑ 𝒘𝒘𝒐𝒐,𝐞𝐞𝟏𝟏𝒑𝒑 𝟐𝟐
𝒐𝒐∈𝑶𝑶 ∗ ∑ 𝒘𝒘𝒐𝒐,𝐞𝐞𝟐𝟐𝒑𝒑 𝟐𝟐
𝒐𝒐∈𝑶𝑶
𝒔𝒔𝒔𝒔𝒔𝒔 𝒆𝒆𝟏𝟏, 𝒆𝒆𝟐𝟐 = ∑ 𝜶𝜶𝒑𝒑 𝒄𝒄𝒐𝒐𝒔𝒔𝒔𝒔𝒔𝒔𝒑𝒑 𝒆𝒆𝟏𝟏, 𝒆𝒆𝟐𝟐 𝒑𝒑∈𝑷𝑷
|𝑷𝑷|
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Events Similarity
Discriminability
Similarity-based Interpolation
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e
o1 p
o2
similar
Interpolation of a fictitious link
p
𝑫𝑫𝒔𝒔𝒔𝒔𝒄𝒄 𝒑𝒑 = 𝒐𝒐 𝒕𝒕𝒕𝒕𝒔𝒔𝒑𝒑𝒍𝒍𝒆𝒆 = < 𝒔𝒔,𝒑𝒑,𝒐𝒐 > ∈ 𝑮𝑮 |
|𝒕𝒕𝒕𝒕𝒔𝒔𝒑𝒑𝒍𝒍𝒆𝒆 = < 𝒔𝒔,𝒑𝒑,𝒐𝒐 > ∈ 𝑮𝑮|
𝑾𝑾 𝒐𝒐𝟐𝟐,𝒆𝒆 𝒑𝒑 = 𝐜𝐜𝐦𝐦𝐦𝐦𝐜𝐜𝐜𝐜𝐜𝐜
𝒐𝒐∈𝑶𝑶𝒑𝒑,𝒆𝒆(𝒐𝒐𝟐𝟐,𝒐𝒐) ∗ 𝒍𝒍𝒐𝒐𝒍𝒍
|𝑬𝑬||𝑬𝑬𝒐𝒐𝟐𝟐,𝒑𝒑|
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Interest Detection
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How to detect user interests from diverse event space?
Latent Dirichlet Allocation (LDA) [Blei et al 2003]
Events
Tags
Topic distribution over each event (T=30)
Attended events Eu
Variance of each topic
User Interest Distribution
Diversity score
Mean of the variances
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Hybrid Recommendation
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Content-based rank:
Hybrid rank
CF rank: Common events between u and RSVP users
αp = property weight βp = interest weight λ cf = CF weight
𝒕𝒕 𝒖𝒖,𝒆𝒆 = 𝒕𝒕𝒄𝒄𝒄𝒄++ 𝒖𝒖,𝒆𝒆 + 𝝀𝝀𝒄𝒄𝒇𝒇 𝒕𝒕𝒄𝒄𝒇𝒇 𝒖𝒖,𝒆𝒆
𝒕𝒕𝒄𝒄𝒄𝒄++ 𝒖𝒖, 𝒆𝒆 =∑ ∑ 𝜶𝜶𝒑𝒑 𝜷𝜷𝒑𝒑 𝒄𝒄𝒐𝒐𝒔𝒔𝒔𝒔𝒔𝒔𝒑𝒑(𝒆𝒆𝒔𝒔 , 𝒆𝒆)𝒑𝒑∈ 𝑷𝑷𝒆𝒆𝒔𝒔 ∈ 𝑬𝑬𝒖𝒖
𝑷𝑷 ∗ |𝑬𝑬𝒖𝒖|
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Experiments
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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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Sparsity Reduction
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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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User Diversity
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Most of users have relatively high interests towards some topics
Score ≈ 1 => strong interest Score ≈ 0 => cold-start users
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Learning weights evaluation
PSO has better performance
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CB+CF evaluation
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Interest Detection 𝛃𝛃𝐜𝐜𝐢𝐢𝐢𝐢𝐞𝐞𝐢𝐢𝐞𝐞𝐜𝐜𝐢𝐢 >
𝟒𝟒 × 𝛃𝛃𝐢𝐢𝐜𝐜−𝐜𝐜𝐢𝐢𝐢𝐢𝐞𝐞𝐢𝐢𝐞𝐞𝐜𝐜𝐢𝐢
High influence of social information in event recommendation
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Comparison with other approaches
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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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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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