Presentation by Valentin Groues Semantic Technologies Seminar, Tudor Research Centre, Luxembourg, 21/03/2011

1. HYbrid semantic and fuzzy approaches
to context-aware PERsonalisation
Valentin Grouès
Supported by the National Research Fund, Luxembourg
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2. HyPer
 Title: HYbrid semantic and fuzzy approaches to context-aware
PERsonalisation
 Supervisors: Dr Yannick Naudet (CRPHT) - Ph.Dr Odej Kao (TuB)
 Hypothesis: The perceived results of personalisation systems can
be improved by combining the reasoning capabilities given by
Semantic Web technologies and the representation of human
imprecisions through fuzzy theory.
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3. Recommender Systems
 How to look for a needle in a haystack?
Just use the appropriate tool
 How to filter and find the needed information in a perpetually
growing amount of data?
 Recommender systems aim at providing personalised suggestions
about items, actions or content considered of interest to the user
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4. Recommender Systems
 Content-based recommender sytems:
– recommend items similar to those the user has previously
liked/experienced
Limitations: Advantages:
- over-specialisation - no cold start for new items
- new user problem - doesn’t require many users, can
- requires good description of work in a one user environment.
items - can provide explanations
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5. Recommender Systems
 Collaborative Filtering (Amazon, Netflix, etc.)
1. Look for users who share a similar rating pattern to that of the active user
2. Use the ratings from like-minded users found in step 1 to calculate a
prediction for a given item.
Limitations: Advantages:
- new user and new item problem - no need for item description
(cold start)
- almost solves the over-
- sparsity problem
specialisation problem of CBF
- grey sheep problem
- good precision
- non diversity problem
- not suitable for items sold only - low-cost capture of complex
once taste mechanisms
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6. Recommender Systems
 Hybrid Systems
 Demographic Filtering (DMF):
– Categorizes the user based on his/her profile to provide
recommendations based on demographic clusters.
– The user will be recommended items similar to the ones other
members of the same demographic characteristics liked.
 Knowledge-based recommender:
– Use a priori domain knowledge to match user requirements
with the properties of items. This approach uses explicit models
of both the users and the products being recommended.
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7. How to improve Recommender Systems?
1. Better methods for representing user behavior and
information about items
2. Focusing on generating an accurate list of
recommendation rather than a list full of individually
accurate recommendations
3. Incorporation of contextual information into
recommendation process
4. Development of less intrusive and more flexible
recommendation methods, explanations
5. Development of recommender system effectiveness
measures
=> Semantics + Context-awareness + Fuzzy Sets
Adomavicius, G. and Tuzhilin, A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on
Knowledge and Data Engineering 17, 6 (2005), 734-749.
Lops, G. , Gemmis, M., Semeraro, G. Content-based Recommender Systems: State of the Art and Trends, in Recommender Systems Handbook, 2010
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8. Need for Semantics
 Semantic ambiguity:
User: u=(Indonesia=0.7;Java=0.9;island=0.2)
Items: d1=(Java=0.4;hotel=0.8), d2=(Java=0.4;software=0.8)
programming
island
language
pref(d1,u)=pref(d2,u)=0.19
Distinction between the two concepts is essential for not
producing undesirable recommendations
Iván Cantador. "Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic-
based Approach". 2008, Madrid
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9. Need for Semantics
 Assumption of terms independance:
User: u=(Indonesia=0.7;Java=0.9;island=0.2)
Items: d1=(Java=0.4;hotel=0.8), d2 =(Java=0.4;archipelago=0.8)
island island
pref(d1,u)=pref(d2,u)=0.19
Semantic relations between concepts have to be considered
Iván Cantador. "Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic-
based Approach". 2008, Madrid
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10. Context awareness
 Mobile environment
 Different situations can correspond to different needs
 Geographical location, time of day, weather, etc.
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11. Fuzzy Sets and Fuzzy Logic
 To represent imprecise information inherent to the
human way of thinking
 Humans have a tendency to use imprecise concepts for
claiming tastes: “cheap restaurant”, “long movie”,
“young actor”, etc.
 Limitations of crisp systems:
– For a user willing to find a restaurant with a cost up to 20€ the system
will equally discard a restaurant costing 21€ as a restaurant costing
300€.
a user would prefer having an answer proportional to
the distance between his ideal preference and the
recommended content
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12. What are Fuzzy Sets?
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13. Our previous research
 Million Dollar Baby recommended
 Unforgiven and The Good, the Bad and the Ugly discarded (westerns)
Naudet, Y., Aghasaryan, A., Toms, Y., & Senot, C. (2008). An Ontology-Based Profiling and Recommending System for Mobile TV. 2008 Third International Workshop on Semantic Media Adaptation and
Personalization (pp. 94-99). IEEE.
Mignon, S., Groues, V., and Naudet, Y. Advanced Personalisation by Ontologies: Audiovisual Content Filtering on Mobile Devices. Journées Francophones des Ontologies, (2008).
Naudet, Y., Mignon, S., Lecaque, L., Hazotte, C., and Groues, V. Ontology-Based Matchmaking Approach for Context-Aware Recommendations. AXMEDIS, (2008).
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14. Semantic similarity measures
 How to compare two instances?
 Medor and Felix have some similarities:
 Common parent, both Mammals
 Similar properties, both 4 legs and same owner
sim(Medor,Felix)=?
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15. Integrating fuzzy sets within ontologies
 FuSOR: A model for representing fuzzy sets and
linguistic values within ontologies (Y. Naudet, V. Grouès, M. Foulonneau,
Introduction to Fuzzy-Ontological Context-Aware Recommendations in Mobile Environments, APRESW 2010)
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16. FuSor: Characteristics of the approach
 Can be used as an extension of an ontology without
requiring any modifications, OWL DL compliant
 Allows using fuzzy sets and their membership functions
for any datatype property
 Supports context and domain dependency
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17. Ex: Describing interest boundaries
Membership functions can be used to define the way a
user interest deviates from an “ideal” value.
Ex: “I am looking for a restaurant with prices up to
20€ but I could accept up to 25€ even if I would be
less satisfied”.
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18. eFoaf
 Cover demographic and basic user information
 Context aware (e.g. not only one contact address)
 Simple and complex interests associated with a context
of validity
 Open to external RDF datasets
 Skills, abilities and handicaps
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19. An application to transport
• Personalisation of carpooling solutions:
– Match carpoolers based on their profiles, their expectations:
• music tastes
• child seat
• animals
• smoking allowed?
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20. An application to transport
• Personalisation of itineraries based on:
– Preferences between means of transportation
– User priorities:
• Cost
• Time
• CO2 footprint
• Touristic interest
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21. An application to transport
• Recommendation in case of an unforeseen event:
– Find an alternative itinerary
– Recommendations based on user profiles:
• Hotels
• Restaurants
• Museum
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22. What’s done?
 FuSOR: an approach to extend existing model to use fuzzy sets
 eFoaf: an extension of foaf to represent rich user profiles and
preferences
 Prototype of recommender system making use of semantics and
fuzzy sets
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23. What’s next?
 Explore other uses of fuzzy sets and fuzzy logic for
recommendations:
– fuzzy sets for item description (this movie belongs to the action
genre with a degree of 0.7, this movie is long)
 Use the list of items liked by the user, history of consumption
 Further development of a prototype applied to a particular use
case (job ads, movies, restaurants)
 Performance optimisation: distributed computing, caching
mechanisms and different semantic web libraries
 Evaluations
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24. Any questions ?
Thank you for your attention!
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