Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 01, January 2021

Making Cross-Domain Recommendations by Associating Disjoint Users and Items Through
the Affective Aware Pseudo Association Method


John Kalung Leung, Igor Griva and William G. Kennedy, George Mason University, USA


This paper utilizes an ingenious text-based affective aware pseudo association method (AAPAM) to link disjoint pseudo 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.


Behavioral Analysis, Emotion-aware Recommender System, Emotion prediction, Personality, Pseudo Users Association.