Dimitrios Rafailidis
Fabio Crestani
ABSTRACT
With the advent of social media, generating high quality cross-domain recommendations has become more and more important for users of heterogeneous domains. In this study, we propose a collaborative ranking model to generate cross-domain recommendations. Given a target domain, we design an objective function aimed at performing push of relevant items at the top of a recommendation list. Also, as users may have different behaviours in multiple domains in our collaborative ranking model we propose a weighting strategy to control the influence of user preferences from auxiliary domains when producing the recommendation lists. Our experiments on ten cross-domain recommendation tasks show that the proposed approach achieves higher recommendation accuracy than other state-of-the-art methods.
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Index Terms
- A Collaborative Ranking Model for Cross-Domain Recommendations
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