Current auxiliary learning methods mainly adopt the methodology of reweighing losses for
the manually collected auxiliary data and tasks. However, these methods heavily rely on …

Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to
alleviate the problem of data sparsity in the target recommendation domain, and has been …

Recommender systems aim to capture users' personalized preferences from the cast
amount of user behaviors, making them pivotal in the era of information explosion. However …

Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to
alleviate the problem of data sparsity in the target recommendation domain, and it has been …

Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in
recommendation systems. While recent CDR approaches using graph neural networks …

This paper presents LightFusionRec, a novel lightweight cross-domain recommendation
system that integrates DistilBERT for textual feature extraction and FastText for genre …