6 th International Workshop on Semantic Media Adaptation and Personalization SMAP 2011

1. Improving e-commerce collaborative recommendations by semantic inference of Neighbors’ practical expertise Manuela I. Martín Vicentemvicente@det.uvigo.es University of Vigo (Spain) 6th International Workshop on Semantic Media Adaptation and Personalization SMAP 2011 Vigo, December 2011

2. Introduction • Recommender systems • Save users from dealing with overwhelming amounts of information, providing them with personalized suggestions. • E-commerce • Collaborative filtering recommenders • Based on offering to the active user items that were appealing to other individuals with similar preferences (neighbors). • Neighborhood formation stage • Two users alike – similar ratings to the same items • Prediction stage • Interest in an item a user does not know – neighbors’ ratings

3. Antecedents and motivation • Expertise – “skill or knowledge that a person has in a particular field” • Considering neighbors’ expertise in the recommendation process can increase accuracy • Problem – lack of information about the skills of the users in a recommender system • Our proposal • Measure of users’ practical expertise • Transparently to users – only requires their consumption history • A semantic approach

4. Domain ontology and user profiles • User modeling by reusing the knowledge formalized in the ontology • IDs to product instances

5. Inference of practical expertise • User’s expertise in a leaf class of products (CL) • Two premises: • Variety of product instances • E.g. Wine – different brands of wine • Variety of attributes of the products • E.g. Wine – from varied countries of origin, produced with diverse grape varieties, of different qualities, etc. • Accordingly, two components: • Quantitative variability • Qualitative variability

6. Quantitative variability • Depends on the number of different instances of CL the user profile stores • Not linked to a specific ontology • Two factors: • Variability – user • Population factor – ontology

7. Qualitative variability (I) • The more diverse attributes of the products (instances of CL) in a user profile, the higher the qualitative variability • Semantic dissimilarity (SemDis) • SemDis attributes • Depth • Least Common Ancestor (LCA) • SemDis instances

8. Qualitative variability (II)

9. Conclusions • Practical measure • On the basis of the products the users have tried • Our strategy exploits users’ consumption histories • Available in any e-commerce recommender system • Reasoning techniques to inspect the semantics of the products stored in the users’ profiles • Considering neighbors’ expertise can greatly improve recommendation results

10. Thank you for your attention!

11. Improving e-commerce collaborative recommendations by semantic inference of Neighbors’ practical expertise Manuela I. Martín Vicentemvicente@det.uvigo.es University of Vigo (Spain) 6th International Workshop on Semantic Media Adaptation and Personalization SMAP 2011 Vigo, December 2011