LightFusionRec: Lightweight Transformers-Based Cross-Domain Recommendation Model

TL;DR

LightFusionRec is a lightweight, cross-domain recommendation model that combines NLP and genre embeddings to improve recommendation accuracy and efficiency, suitable for on-device use and addressing common issues like data sparsity and cold start.

Contribution

It introduces a novel lightweight architecture integrating DistilBERT and FastText for enhanced cross-domain recommendations with improved efficiency and scalability.

Findings

Significant improvement in recommendation quality over traditional methods

Effective handling of data sparsity and cold start problems

Enables on-device inference for practical deployment

Abstract

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.