The work in this thesis addresses the new challenges and opportunities for online personalisation posed by the emergence of new infrastructures for sharing user preferences and for access to open …
The work in this thesis addresses the new challenges and opportunities for online personalisation posed by the emergence of new infrastructures for sharing user preferences and for access to open repositories of data. As a result of these new infrastructures, user profiles can now include data from multiple sources about preferences in multiple domains. This new kind of user profile data requires a cross-domain personalisation approach. However, current cross-domain personalisation approaches are restricted to proprietary social networking ecosystems.
The main problem that we address in this thesis, is to enable cross-domain recommendations without the use of proprietary and closed infrastructure. Towards this goal, we propose an open framework for cross-domain personalisation. Our framework consists of two parts: a conceptual architecture for recommender systems, and our cross-domain personalisation approach. The main enabling technology for our framework is Linked Open Data, as it provides a common data presentation for user preferences and cross-domain links between concepts from many different domains.
As part of our framework, we first propose a conceptual architecture for Linked Open Data recommender systems that provides guidelines and best practices for the typical high level components required for providing personalisation in open ecosystems using Linked Open Data. The architecture has a strong empirical founding, as it based on an empirical survey of 124 RDF-based applications.
Then we introduce and throughly evaluate SemStim, an unsupervised, graph-based algorithm for cross-domain personalisation. It leverages multi-source, domain-neutral user profiles and the semantic network of DBpedia in order to generate recommendations for different source and target domains. The results of our evaluation show that SemStim is able to provide cross-domain recommendations, without any overlap between target and source domains and without using any ratings in the target domain.
We show how we instantiate our proposed conceptual architecture for a prototype implementation that is the outcome of the ADVANSSE collaboration project with CISCO Galway. The prototype shows how to implement our framework for a real-world use case and data.
Our open framework for cross-domain personalisation provides an alternative to existing proprietary cross-domain personalisation approaches. As such, it opens up the potential for novel and innovative personalised services without the risk of user lock-in and data silos.