HYbrid semantic and fuzzy approaches to context-aware PERsonalisation

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

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HYbrid semantic and fuzzy approaches to context-aware PERsonalisation

  1. 1. HYbrid semantic and fuzzy approachesto context-aware PERsonalisation Valentin Grouès Supported by the National Research Fund, Luxembourg 1
  2. 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. 2
  3. 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 perpetuallygrowing amount of data? Recommender systems aim at providing personalised suggestionsabout items, actions or content considered of interest to the user 3
  4. 4. Recommender Systems Content-based recommender sytems: – recommend items similar to those the user has previously liked/experiencedLimitations: 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 4
  5. 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 5
  6. 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. 6
  7. 7. How to improve Recommender Systems?1. Better methods for representing user behavior and information about items2. Focusing on generating an accurate list of recommendation rather than a list full of individually accurate recommendations3. Incorporation of contextual information into recommendation process4. Development of less intrusive and more flexible recommendation methods, explanations5. Development of recommender system effectiveness measures => Semantics + Context-awareness + Fuzzy SetsAdomavicius, G. and Tuzhilin, A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions onKnowledge 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 7
  8. 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 8
  9. 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 9
  10. 10. Context awareness Mobile environment Different situations can correspond to different needs Geographical location, time of day, weather, etc. 10
  11. 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 tothe distance between his ideal preference and therecommended content 11
  12. 12. What are Fuzzy Sets? 12
  13. 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 andPersonalization (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). 13
  14. 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)=? 14
  15. 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) 15
  16. 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 16
  17. 17. Ex: Describing interest boundariesMembership functions can be used to define the way auser interest deviates from an “ideal” value.Ex: “I am looking for a restaurant with prices up to20€ but I could accept up to 25€ even if I would beless satisfied”. 17
  18. 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 18
  19. 19. An application to transport• Personalisation of carpooling solutions: – Match carpoolers based on their profiles, their expectations: • music tastes • child seat • animals • smoking allowed? 19
  20. 20. An application to transport• Personalisation of itineraries based on: – Preferences between means of transportation – User priorities: • Cost • Time • CO2 footprint • Touristic interest 20
  21. 21. An application to transport• Recommendation in case of an unforeseen event: – Find an alternative itinerary – Recommendations based on user profiles: • Hotels • Restaurants • Museum 21
  22. 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 22
  23. 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 23
  24. 24. Any questions ?Thank you for your attention! 24

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