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

arXiv: https://arxiv.org/abs/2012.05982 · HTML: https://mlsanity.com/paper/2012.05982/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Reading the Mood: Emotion-Guided Book-to-Music Recommendation via CGANs and LLMs](https://mlsanity.com/paper/2610.06703/) (2026)

## Abstract (arXiv)

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.