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

TL;DR

This paper introduces AAPAM, a novel affective-aware pseudo association method that links disjoint users and items across domains, enabling effective cross-domain recommendations without extra retrieval protocols.

Contribution

The paper presents AAPAM, a new method that seamlessly joins datasets from different domains for improved cross-domain content and collaborative filtering.

Findings

AAPAM effectively links disjoint datasets across domains.

The method eliminates the need for additional cross-domain retrieval protocols.

AAPAM reduces cold start issues and enhances serendipitous recommendations.

Abstract

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.