In past years, the OpenAI's Scaling-Laws shows the amazing intelligence with the next-token prediction paradigm in neural language modeling, which pointing out a free-lunch way to enhance the model performance by scaling the model parameters. In RecSys, the retrieval stage is also follows a 'next-token prediction' paradigm, to recall the hunderds of items from the global item set, thus the generative recommendation usually refers specifically to the retrieval stage (without Tree-based methods). This raises a philosophical question: without a ground-truth next item, does the generative recommendation also holds a potential scaling law? In retrospect, the generative recommendation has two different technique paradigms: (1) ANN-based framework, utilizing the compressed user embedding to retrieve nearest other items in embedding space, e.g, Kuaiformer. (2) Auto-regressive-based framework, employing the beam search to decode the item from whole space, e.g, OneRec. In this paper, we devise a unified encoder-decoder framework to validate their scaling-laws at same time. Our empirical finding is that both of their losses strictly adhere to power-law Scaling Laws ($R^2$>0.9) within our unified architecture.

翻译：近年来，OpenAI的缩放定律通过神经网络语言建模中的下一词元预测范式展现了惊人的智能，指出了一条通过扩展模型参数来提升模型性能的“免费午餐”之路。在推荐系统中，召回阶段同样遵循“下一词元预测”范式，旨在从全局物品集合中召回数百个物品，因此生成式推荐通常特指召回阶段（不含基于树的方法）。这引发了一个哲学问题：在没有真实下一物品的情况下，生成式推荐是否也存在潜在的缩放定律？回顾历史，生成式推荐存在两种不同的技术范式：（1）基于近似最近邻的框架，利用压缩的用户嵌入在嵌入空间中检索最邻近的其他物品，例如Kuaiformer。（2）基于自回归的框架，采用束搜索从整个空间中解码物品，例如OneRec。本文设计了一个统一的编码器-解码器框架，以同时验证这两种范式的缩放定律。我们的实证发现是：在统一架构下，两者的损失均严格遵循幂律缩放定律（$R^2$>0.9）。