ResearchPod Summary
Generative recommendation models often rely on item tokenization to convert continuous item embeddings into discrete identifiers. However, the standard quantization process (such as RQ-VAE) often disrupts the intrinsic semantic relationships between items—a phenomenon the authors call topology distortion. This paper asks: how can we preserve these relational structures during the tokenization process to improve the accuracy of generative recommendations?
The authors propose Topology-Aware Tokenization (TopoTok), a framework that integrates hierarchical topology distillation into the residual quantization process. Instead of applying a single, monolithic supervision signal, TopoTok decomposes the distillation objective into three granular levels that align with the hierarchical nature of the RQ-VAE:
By transforming distance matrices into similarity distributions, the model aligns the relative ranking of neighbors in the continuous embedding space with the discrete token space, ensuring that the structural integrity of the item manifold is maintained.
In generative recommendation, the model's ability to predict the next item depends heavily on its perception of item similarity. When tokenization distorts the underlying topology, the model receives "topological noise," leading to incorrect recommendations. TopoTok provides a systematic way to ensure that discrete identifiers remain faithful to the original semantic space, resulting in performance gains of up to 9.42% in Recall@5 on benchmark datasets. This approach demonstrates that careful management of the tokenization process is as critical as the choice of the generative model itself.
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