SPAR: Personalized Content-Based Recommendation via Long Engagement
  Attention
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.
65d2dc3dc6f238f8687cd62e
Chiyu Zhang
admin_assigned
2024-02-19T15:28:27.272Z
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chiyu_zhang
user
chiyuzhang
65d2dc3dc6f238f8687cd62f
Yifei Sun
claimed_verified
2024-02-19T17:05:24.217Z
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Yifei Sun
user
sunyifei
65d2dc3dc6f238f8687cd630
Jun Chen
admin_assigned
2024-02-19T15:30:13.784Z
62f94ec077b722f186641192
/avatars/37f4c7f2b6f59e2eac2d1b1b5cc88269.svg
jun chen
user
junchen14
65d2dc3dc6f238f8687cd631
Jie Lei
admin_assigned
2024-02-19T15:30:21.278Z
64e7be5c0c47bf287ca5baf5
/avatars/a82dec23bde346f1479049f74e55004b.svg
Jie Lei
user
jielei
65d2dc3dc6f238f8687cd632
Muhammad Abdul-Mageed
admin_assigned
2024-02-19T15:30:37.096Z
5feeae8dd8e0b2d24ed258cc
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Muhammad Abdul-Mageed
user
mageed
65d2dc3dc6f238f8687cd633
Sinong Wang
admin_assigned
2024-02-19T15:30:43.635Z
65b483c5ed110eb9f1ee62df
/avatars/29100098f5aed1735675d06c516a85b7.svg
Theron S. Wang
user
TheronWong
65d2dc3dc6f238f8687cd634
Rong Jin
65d2dc3dc6f238f8687cd635
Sem Park
65d2dc3dc6f238f8687cd636
Ning Yao
65d2dc3dc6f238f8687cd637
Bo Long
SPAR: Personalized Content-Based Recommendation via Long Engagement
  Attention
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.
The SPAR framework enhances content recommendations by using pretrained language models, poly-attention layers, and large language models to effectively process long user engagement histories and predict user-item interactions.
pretrained language models poly-attention layers attention sparsity mechanisms large language models