Cross-domain recommendations are currently available in closed, proprietary social networking ecosystems such as Facebook, Twitter and Google+. I propose an open framework as an alternative, which enables cross-domain recommendations with domain-agnostic user profiles modelled as semantic interest graphs. This novel framework covers all parts of a recommender system. It includes an architecture for privacy-enabled profile exchange, a distributed and domainagnostic user model and a cross-domain recommendation algorithm. This enables users to receive recommendations for a target domain (e.g. food) based on any kind of previous interests.
Heitmann REF proposed to conduct cross-domain personalization and recommendation based on multi-source semantic interest graphs.
An open framework for multi-source, cross-domain personalisation with semantic interest graphs
RecSys '12
Computer Science