IMPROVING E-COMMERCE COLLABORATIVE RECOMMENDATIONS BY SEMANTIC INFERENCE OF NEIGHBORS’ PRACTICAL EXPERTISE 6 th International Workshop on Semantic Media Adaptation and Personalization SMAP 2011 Vigo, December 2011 Manuela I. Martín Vicente University of Vigo (Spain)

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 2

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 3

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

Inference of practical expertise 5  User’s expertise in a leaf class of products (C L )  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

Quantitative variability 6  Depends on the number of different instances of C L the user profile stores  Not linked to a specific ontology  Two factors:  Variability – user  Population factor – ontology

Qualitative variability (I)  The more diverse attributes of the products (instances of C L ) in a user profile, the higher the qualitative variability  Semantic dissimilarity (SemDis) 1. SemDis attributes Depth Least Common Ancestor (LCA) 2. SemDis instances 7

Qualitative variability (II) 8

Conclusions 9  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

Thank you for your attention! 10

IMPROVING E-COMMERCE COLLABORATIVE RECOMMENDATIONS BY SEMANTIC INFERENCE OF NEIGHBORS’ PRACTICAL EXPERTISE 6 th International Workshop on Semantic Media Adaptation and Personalization SMAP 2011 Vigo, December 2011 Manuela I. Martín Vicente University of Vigo (Spain)