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?

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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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