Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation (2503.22675v2)

Abstract: 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 \textbf{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.

Unleashing the Latent Reasoning Power for Sequential Recommendation

The paper "Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation" presents an innovative approach to sequential recommendation that aims to harness latent reasoning capabilities during inference. In traditional recommender systems, sequential recommendation models predict the next item by analyzing historical user interactions. These models predominantly employ direct forward computation, taking the final hidden state of sequence encoders as user representations. However, this standard approach is limited by its computational depth, which hampers the ability to model complex user preference dynamics and often fails to accurately represent long-tail items.

To address these limitations, the authors propose ReaRec, a new framework for inference-time computing tailored to recommender systems. ReaRec enhances user representations by incorporating implicit multi-step reasoning, which allows deeper feature crossing in latent space, offering a more comprehensive understanding of user interests. The autoregressive mechanism feeds the sequence's last hidden state back into the sequential recommender, accompanied by special reasoning position embeddings. These embeddings serve to dissociate the original item encoding from the newly introduced multi-step reasoning space.

The paper further introduces two learning strategies: Ensemble Reasoning Learning (ERL) and Progressive Reasoning Learning (PRL), designed to capitalize on ReaRec's reasoning capabilities. ERL adopts ensemble learning principles to form multi-order user representations, providing diverse perspectives on user interests. In contrast, PRL utilizes a progressive temperature annealing mechanism inspired by curriculum learning to refine the model's understanding of evolving user patterns over successive reasoning steps.

Extensive experimental evaluations on five public datasets demonstrate ReaRec's effectiveness and adaptability across different SeqRec architectures, with notable improvements in recommendation performance. For instance, the framework elevates the performance ceiling of SeqRec models by approximately 30\%-50%. Such findings underscore the potential of inference-time computational extensions to transform sequential recommendation methodologies.

Implications and Future Directions

The introduction of ReaRec signifies a substantial shift towards incorporating latent reasoning into recommendation systems, with significant implications for improving user satisfaction through more accurately capturing complex preference distributions. On a practical level, this method offers a robust framework for handling sparse user interactions and unpopular items, scenarios where conventional models often struggle.

The theoretical insights from this work suggest future exploration into inference-time reasoning paradigms could yield further advancements in recommendation performance, especially when considering differentiated reasoning mechanisms tailored to user activity levels and item popularity. Furthermore, optimizing inference-time computation to balance model accuracy and efficiency remains a critical area of development.

In conclusion, the innovative approach of embedding reasoning within sequential recommendation not only addresses current modeling limitations but also opens avenues for deeper research at the intersection of AI reasoning and recommendation systems. As researchers continue to explore inference-time computing frameworks, the ability to adaptively adjust reasoning depth and effectively disentangle reasoning from encoding processes will likely define the next generation of recommender system technologies.