STN-CDRS: Sentiment Transfer Network for Cross-Domain Recommendation Systems

  • Nikita Taneja Computer Science and Technology, Manav Rachna University, Faridabad, India
  • Hardeo Kumar Thakur School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India


In enterprise environments, the products may come from a variety of categories or domains. Users may engage with entities in one domain, but not in the others when they are presented with multiple domains. Such users are referred to as “cold-starters” in other domains. The primary difficulty in cross-domain recommendation systems is to efficiently transfer user’s latent information based on their engagements in one domain into the other domains. The advancements in recommendation systems have inspired us to develop review-driven recommendation models that utilize cross-domain knowledge transfer and deep learning models. This work proposes a sentiment transfer network specifically designed for providing recommendation in cross-domain (STN-CDRS). The novelty of the work lies in the user rating enrichment mechanism, which is done by extracting latent information from user review data to fill sparse rating matrix. This enrichment uses previously developed RNN-Core method for efficiently learning user reviews. The reviews provided by the users are used to enrich sparse data across domains. This enrichment allows two things: alleviates the cold start problem and allows more intersecting users across domains to bridge the gap while learning. This work empirically demonstrates its efficiency by iteratively updating over the baseline recommendation models in terms of MAE (mean absolute error), RMSD (root mean squared deviation), precision and recall measures with other state-of-the-art-review-aided cross-domain recommendation systems.


cross-domain recommendations, sentiment transfer network, user reviews, deep learning, knowledge transfer,


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Jun 19, 2024
How to Cite
TANEJA, Nikita; THAKUR, Hardeo Kumar. STN-CDRS: Sentiment Transfer Network for Cross-Domain Recommendation Systems. Computer Assisted Methods in Engineering and Science, [S.l.], june 2024. ISSN 2956-5839. Available at: <>. Date accessed: 18 july 2024. doi:
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