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

Authors

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

Abstract

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.

Keywords

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

Full Text  Volume 11, Number 1