Using Affective Aware Pseudo Association Method to Connect Disjoint Users and Items for Making Cross-Domain Recommendations

John Kalung Leung, Igor Griva, William G. Kennedy

This paper introduces an ingenious text-based affective aware pseudo association method (AAPAM) to connect disjoint users and items across different information domains and leverage them to make cross-domain content-based and collaborative filtering recommendations. This paper demonstrates that the AAPAM method could seamlessly join different information domain datasets to act as one without additional cross-domain information retrieval protocols. Besides making cross-domain recommendations, the benefit of joining datasets from different information domains through AAPAM is that it eradicates cold start issues while making serendipitous recommendations.

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