# LightFusionRec: Lightweight Transformers-Based Cross-Domain Recommendation Model

arXiv: https://arxiv.org/abs/2410.15656 · HTML: https://mlsanity.com/paper/2410.15656/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [Graph Transformer for Recommendation](https://mlsanity.com/paper/2306.02330/) (2023)
- [Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck](https://mlsanity.com/paper/2203.16863/) (2022)
- [A Privacy Preserving System for Movie Recommendations Using Federated Learning](https://mlsanity.com/paper/2303.04689/) (2023)

## Abstract (arXiv)

This paper presents LightFusionRec, a novel lightweight cross-domain recommendation system that integrates DistilBERT for textual feature extraction and FastText for genre embedding. Important issues in recommendation systems, such as data sparsity, computational efficiency, and cold start issues, are addressed in methodology. LightFusionRec uses a small amount of information to produce precise and contextually relevant recommendations for many media formats by fusing genre vector embedding with natural language processing algorithms. Tests conducted on extensive movie and book datasets show notable enhancements in suggestion quality when compared to conventional methods. Because of its lightweight design, the model can be used for a variety of purposes and allows for ondevice inference. LightFusionRec is a noteworthy development in cross-domain recommendation systems, providing accurate and scalable recommendations to improve user experience on digital content platforms.