Reproduce “LARES: Latent Reasoning for Sequential Recommendation” on OpenResearch
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About this paper
Enze Liu, Bowen Zheng, Xiaolei Wang, Wayne Xin Zhao, Jinpeng Wang, Sheng Chen, Ji-Rong Wen
A depth-recurrent architecture called LARES enhances sequential recommender systems by performing multi-step implicit reasoning within the latent space, refining all input tokens at each iteration. This framework consistently improves recommendation accuracy by up to 21.4% on NDCG@10 compared to state-of-the-art baselines across real-world datasets.
Key insights
- Scaling test-time computation through iterative latent reasoning is an effective approach for enhancing performance in sparse recommendation environments, offering an alternative to parameter scaling.
- Refining all input tokens at each reasoning step, rather than just one, significantly maximizes computational utilization and deepens the representational power within the latent space.
- A two-stage training strategy combining self-supervised alignment and reinforcement learning provides a robust method for supervising implicit, multi-step latent reasoning.
Summary by alphaXiv.
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