Recommendations using DBpedia: How your Facebook profile can be used to find your next greeting card
by Anne van de Venis
Recommender systems (RS) are systems that provide suggestions that users may find interesting. In this thesis we present our Interest-Based Recommender System (IBRS) that can recommend tagged item sets from any domain. This RS is validated with item sets from two different domains, namely postcards and holidays homes. While postcards and holiday homes are very different items, with different characteristics, IBRS uses the same recommender engine to create recommendations. IBRS solves several problems that are present in classic RSs, such as the cold-start problem and language independence. The cold-start problem for new users, is solved by using Facebook likes for creating a user profile. It uses information in DBpedia to create recommendations in a tag-based item set for multiple domains, independent of the language. Using both external knowledge sources and user content, makes our system a hybrid of a knowledge-based and content-based RS. We validated our system through an online evaluation system in two evaluation rounds with test user groups of approximately 71 and 44 people. The main contributions in this thesis are:
- a literature study of existing recommendation approaches;
- a language-independent mapping approach for tags and social media resource onto DBpedia resources;
- a domain-independent algorithm for detecting related concepts in the DBpedia graph;
- a recommendation approach based on both Facebook and DBpedia;
- a validation of our recommendation approach.