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<p>To tackle the persistent data sparsity issue in recommendation systems (RSs), cross-domain recommendation has emerged as an effective solution by leveraging data from richer domains to enhance performance in sparser ones. Our proposed framework not only addresses data limitations but also ensures that the recommendation process remains fair, transparent and user-centered. By employing contrastive learning and multi-head attention, proposed framework creates uniform and unbiased user/item embeddings, counteracting issues of popularity bias and embedding inconsistencies. A critical aspect of this framework is its behavior encoder, designed to learn user preferences through behavioral data such as clicks, reviews and sentiments ensuring a personalized experience for users even in domains with sparse data. To maintain fairness and transparency, the system adaptively balances user domain-specific and domain-shared features based on user preferences in target domain.</p>

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Cross-domain recommendation framework for enhanced personalization through contrastive learning

  • 期刊论文

  • 发表日期：

    2026年1月11日

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  • Rabia Khan,
  • Naima Iltaf,
  • Rabia Latif,
  • Nor Shahida Mohd Jamail

摘要

To tackle the persistent data sparsity issue in recommendation systems (RSs), cross-domain recommendation has emerged as an effective solution by leveraging data from richer domains to enhance performance in sparser ones. Our proposed framework not only addresses data limitations but also ensures that the recommendation process remains fair, transparent and user-centered. By employing contrastive learning and multi-head attention, proposed framework creates uniform and unbiased user/item embeddings, counteracting issues of popularity bias and embedding inconsistencies. A critical aspect of this framework is its behavior encoder, designed to learn user preferences through behavioral data such as clicks, reviews and sentiments ensuring a personalized experience for users even in domains with sparse data. To maintain fairness and transparency, the system adaptively balances user domain-specific and domain-shared features based on user preferences in target domain.