title: Think Before Recommend: Unleashing the Latent Reasoning Power for
    Sequential Recommendation
summary: 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.
paper_authors._id: 67e9f6b7d13d75fc155c7f2e
paper_authors.name: Jiakai Tang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-31T08:11:47.198Z
paper_authors._id: 65acfb3a14e6582c30b4ce76
paper_authors.avatarUrl: /avatars/3402ba72fe2436a9c2c2f92e56b15deb.svg
paper_authors.fullname: TangJiakai
paper_authors.type: user
paper_authors.user: TangJiakai5704
paper_authors._id: 67e9f6b7d13d75fc155c7f2f
paper_authors.name: Sunhao Dai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:23:02.371Z
paper_authors._id: 64db88993725f8d9a908c077
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64db88993725f8d9a908c077/JZEGk0kius6mrANlwOWw9.jpeg
paper_authors.fullname: Sunhao Dai
paper_authors.type: user
paper_authors.user: KID-22
paper_authors._id: 67e9f6b7d13d75fc155c7f30
paper_authors.name: Teng Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:23:10.725Z
paper_authors._id: 66152fbe1bcd61054402449b
paper_authors.avatarUrl: /avatars/17cb2f997e7983d706d87cf7c8c5c3dd.svg
paper_authors.fullname: Shi
paper_authors.type: user
paper_authors.user: TengShi
paper_authors._id: 67e9f6b7d13d75fc155c7f31
paper_authors.name: Jun Xu
paper_authors._id: 67e9f6b7d13d75fc155c7f32
paper_authors.name: Xu Chen
paper_authors._id: 67e9f6b7d13d75fc155c7f33
paper_authors.name: Wen Chen
paper_authors._id: 67e9f6b7d13d75fc155c7f34
paper_authors.name: Wu Jian
paper_authors._id: 67e9f6b7d13d75fc155c7f35
paper_authors.name: Yuning Jiang
paper_title: Think Before Recommend: Unleashing the Latent Reasoning Power for
    Sequential Recommendation
paper_summary: 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.
paper_ai_summary: ReaRec enhances sequential recommendation by using inference-time multi-step reasoning, improving model performance on long-tail items and user preference evolution.
paper_ai_keywords: Sequential Recommendation SeqRec inference-time computing framework user representation sequence encoder autoregressive reasoning position embeddings Ensemble Reasoning Learning Progressive Reasoning Learning