Think Before Recommend: Unleashing the Latent Reasoning Power for
  Sequential Recommendation
Sequential Recommendation (SeqRec) aims to predict the next item by capturing
sequential patterns from users' historical interactions, playing a crucial role
in many real-world recommender systems. However, existing approaches
predominantly adopt a direct forward computation paradigm, where the final
hidden state of the sequence encoder serves as the user representation. We
argue that this inference paradigm, due to its limited computational depth,
struggles to model the complex evolving nature of user preferences and lacks a
nuanced understanding of long-tail items, leading to suboptimal performance. To
address this issue, we propose ReaRec, the first inference-time
computing framework for recommender systems, which enhances user
representations through implicit multi-step reasoning. Specifically, ReaRec
autoregressively feeds the sequence's last hidden state into the sequential
recommender while incorporating special reasoning position embeddings to
decouple the original item encoding space from the multi-step reasoning space.
Moreover, we introduce two lightweight reasoning-based learning methods,
Ensemble Reasoning Learning (ERL) and Progressive Reasoning Learning (PRL), to
further effectively exploit ReaRec's reasoning potential. Extensive experiments
on five public real-world datasets and different SeqRec architectures
demonstrate the generality and effectiveness of our proposed ReaRec.
Remarkably, post-hoc analyses reveal that ReaRec significantly elevates the
performance ceiling of multiple sequential recommendation backbones by
approximately 30\%-50\%. Thus, we believe this work can open a new and
promising avenue for future research in inference-time computing for sequential
recommendation.
67e9f6b7d13d75fc155c7f2e
Jiakai Tang
claimed_verified
2025-03-31T08:11:47.198Z
65acfb3a14e6582c30b4ce76
https://cdn-avatars.huggingface.co/v1/production/uploads/65acfb3a14e6582c30b4ce76/RhEhePggBtyM0RIIqXQen.jpeg
TangJiakai
user
TangJiakai5704
67e9f6b7d13d75fc155c7f2f
Sunhao Dai
admin_assigned
2025-03-31T08:23:02.371Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64db88993725f8d9a908c077/JZEGk0kius6mrANlwOWw9.jpeg
Sunhao Dai
user
KID-22
67e9f6b7d13d75fc155c7f30
Teng Shi
admin_assigned
2025-03-31T08:23:10.725Z
66152fbe1bcd61054402449b
/avatars/17cb2f997e7983d706d87cf7c8c5c3dd.svg
Shi
user
TengShi
67e9f6b7d13d75fc155c7f31
Jun Xu
67e9f6b7d13d75fc155c7f32
Xu Chen
67e9f6b7d13d75fc155c7f33
Wen Chen
67e9f6b7d13d75fc155c7f34
Wu Jian
67e9f6b7d13d75fc155c7f35
Yuning Jiang
Think Before Recommend: Unleashing the Latent Reasoning Power for
  Sequential Recommendation
Sequential Recommendation (SeqRec) aims to predict the next item by capturing
sequential patterns from users' historical interactions, playing a crucial role
in many real-world recommender systems. However, existing approaches
predominantly adopt a direct forward computation paradigm, where the final
hidden state of the sequence encoder serves as the user representation. We
argue that this inference paradigm, due to its limited computational depth,
struggles to model the complex evolving nature of user preferences and lacks a
nuanced understanding of long-tail items, leading to suboptimal performance. To
address this issue, we propose ReaRec, the first inference-time
computing framework for recommender systems, which enhances user
representations through implicit multi-step reasoning. Specifically, ReaRec
autoregressively feeds the sequence's last hidden state into the sequential
recommender while incorporating special reasoning position embeddings to
decouple the original item encoding space from the multi-step reasoning space.
Moreover, we introduce two lightweight reasoning-based learning methods,
Ensemble Reasoning Learning (ERL) and Progressive Reasoning Learning (PRL), to
further effectively exploit ReaRec's reasoning potential. Extensive experiments
on five public real-world datasets and different SeqRec architectures
demonstrate the generality and effectiveness of our proposed ReaRec.
Remarkably, post-hoc analyses reveal that ReaRec significantly elevates the
performance ceiling of multiple sequential recommendation backbones by
approximately 30\%-50\%. Thus, we believe this work can open a new and
promising avenue for future research in inference-time computing for sequential
recommendation.
ReaRec enhances sequential recommendation by using inference-time multi-step reasoning, improving model performance on long-tail items and user preference evolution.
Sequential Recommendation SeqRec inference-time computing framework user representation sequence encoder autoregressive reasoning position embeddings Ensemble Reasoning Learning Progressive Reasoning Learning