title: SPAR: Personalized Content-Based Recommendation via Long Engagement
    Attention
summary: Leveraging users' long engagement histories is essential for personalized
  content recommendations. The success of pretrained language models (PLMs) in
  NLP has led to their use in encoding user histories and candidate items,
  framing content recommendations as textual semantic matching tasks. However,
  existing works still struggle with processing very long user historical text
  and insufficient user-item interaction. In this paper, we introduce a
  content-based recommendation framework, SPAR, which effectively tackles the
  challenges of holistic user interest extraction from the long user engagement
  history. It achieves so by leveraging PLM, poly-attention layers and attention
  sparsity mechanisms to encode user's history in a session-based manner. The
  user and item side features are sufficiently fused for engagement prediction
  while maintaining standalone representations for both sides, which is efficient
  for practical model deployment. Moreover, we enhance user profiling by
  exploiting large language model (LLM) to extract global interests from user
  engagement history. Extensive experiments on two benchmark datasets demonstrate
  that our framework outperforms existing state-of-the-art (SoTA) methods.
paper_authors._id: 65d2dc3dc6f238f8687cd62e
paper_authors.name: Chiyu Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: chiyu_zhang
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paper_authors.user: chiyuzhang
paper_authors._id: 65d2dc3dc6f238f8687cd62f
paper_authors.name: Yifei Sun
paper_authors.status: claimed_verified
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paper_authors.fullname: Yifei Sun
paper_authors.type: user
paper_authors.user: sunyifei
paper_authors._id: 65d2dc3dc6f238f8687cd630
paper_authors.name: Jun Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: jun chen
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paper_authors.user: junchen14
paper_authors._id: 65d2dc3dc6f238f8687cd631
paper_authors.name: Jie Lei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-19T15:30:21.278Z
paper_authors._id: 64e7be5c0c47bf287ca5baf5
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paper_authors.fullname: Jie Lei
paper_authors.type: user
paper_authors.user: jielei
paper_authors._id: 65d2dc3dc6f238f8687cd632
paper_authors.name: Muhammad Abdul-Mageed
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-19T15:30:37.096Z
paper_authors._id: 5feeae8dd8e0b2d24ed258cc
paper_authors.avatarUrl: /avatars/1d66840db4ed6dc12bdee7ba8168ea9f.svg
paper_authors.fullname: Muhammad Abdul-Mageed
paper_authors.type: user
paper_authors.user: mageed
paper_authors._id: 65d2dc3dc6f238f8687cd633
paper_authors.name: Sinong Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-19T15:30:43.635Z
paper_authors._id: 65b483c5ed110eb9f1ee62df
paper_authors.avatarUrl: /avatars/29100098f5aed1735675d06c516a85b7.svg
paper_authors.fullname: Sinong Wang
paper_authors.type: user
paper_authors.user: TheronWong
paper_authors._id: 65d2dc3dc6f238f8687cd634
paper_authors.name: Rong Jin
paper_authors._id: 65d2dc3dc6f238f8687cd635
paper_authors.name: Sem Park
paper_authors._id: 65d2dc3dc6f238f8687cd636
paper_authors.name: Ning Yao
paper_authors._id: 65d2dc3dc6f238f8687cd637
paper_authors.name: Bo Long
paper_title: SPAR: Personalized Content-Based Recommendation via Long Engagement
    Attention
paper_summary: Leveraging users' long engagement histories is essential for personalized
  content recommendations. The success of pretrained language models (PLMs) in
  NLP has led to their use in encoding user histories and candidate items,
  framing content recommendations as textual semantic matching tasks. However,
  existing works still struggle with processing very long user historical text
  and insufficient user-item interaction. In this paper, we introduce a
  content-based recommendation framework, SPAR, which effectively tackles the
  challenges of holistic user interest extraction from the long user engagement
  history. It achieves so by leveraging PLM, poly-attention layers and attention
  sparsity mechanisms to encode user's history in a session-based manner. The
  user and item side features are sufficiently fused for engagement prediction
  while maintaining standalone representations for both sides, which is efficient
  for practical model deployment. Moreover, we enhance user profiling by
  exploiting large language model (LLM) to extract global interests from user
  engagement history. Extensive experiments on two benchmark datasets demonstrate
  that our framework outperforms existing state-of-the-art (SoTA) methods.
paper_ai_keywords: pretrained language models (PLMs) textual semantic matching tasks poly-attention layers attention sparsity mechanisms session-based manner large language model (LLM)