Making Cross-Domain Recommendations by Associating Disjoint Users and Items Through the Affective Aware Pseudo Association Method
This method aims to improve recommendation systems for users by enabling cross-domain recommendations and mitigating cold start problems.
This paper proposes the Affective Aware Pseudo Association Method (AAPAM) to connect disjoint users and items across different information domains. This method enables cross-domain content-based and collaborative filtering recommendations, and also addresses cold start issues and facilitates serendipitous recommendations.
This paper utilizes an ingenious text-based affective aware pseudo association method (AAPAM) to link 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 any 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.