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Towards popularity-aware recommendation: A multi-behavior enhanced framework with orthogonality constraint

Author

Listed:
  • Han, Yishan
  • Xu, Biao
  • Wang, Yao
  • Gao, Shanxing

Abstract

Top-K recommendation involves inferring latent user preferences and generating personalized recommendations accordingly, which is now ubiquitous in various decision systems. Nonetheless, recommender systems usually suffer from severe popularity bias, leading to the over-recommendation of popular items. Such a bias deviates from the central aim of reflecting user preference faithfully, compromising both customer satisfaction and retailer profits. Despite the prevalence, existing methods tackling popularity bias still have limitations due to the considerable accuracy-debias tradeoff and the sensitivity to extensive parameter selection, further exacerbated by the extreme sparsity in positive user-item interactions.

Suggested Citation

  • Han, Yishan & Xu, Biao & Wang, Yao & Gao, Shanxing, 2026. "Towards popularity-aware recommendation: A multi-behavior enhanced framework with orthogonality constraint," Omega, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:jomega:v:140:y:2026:i:c:s0305048325002014
    DOI: 10.1016/j.omega.2025.103475
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    References listed on IDEAS

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