Improving end-system recommender systems using cross-platform personal information - Alanazi, Sultan (2017)
Today, the web is constantly growing, expanding global information space and more and more data is being processed and sourced online. The amount of electronically accessible and available online information is overwhelming. Increasingly, recommendation systems, which engage in some form of automated personalisation, are hugely prevalent on the web and have been extensively studied in the research literature. Several issues still remain unsolved including high sparsity situation and cold starts (how to recommend content to users who have had little or no prior interaction with the system). Recent work has demonstrated a potential solution in the form of cross-domain user modeling. This thesis will explore the design, implementation and testing of a cross-domain approach using social media data to model rich and effective user preferences and provide empirical evidence of the effectiveness of the approach based on direct real-world user feedback, deconstructing a cross-system news recommendation service where user models are generated via social media data. This will be accomplished by identifying the availability of a source domain from which to draw resources for recommendations and the availability of user profiles that capture a wide range of user interests from different domains. This thesis also demonstrates the viability of generating user models from social media data and evidences that the automated cross-domain approach can be superior to explicit filtering using self-declared preferences and can be further augmented when placing the user with the ability to maintain control over such models. The reasons for these results are qualitatively examined in order to understand why such effects occur, indicating that different models are capturing widely different areas within a user's preference space.