Implicit Multi-Behavior Generative Recommendation With Mixture of Quantization

doi:10.1109/TKDE.2025.3572014

Implicit Multi-Behavior Generative Recommendation With Mixture of Quantization

2025-08-01

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DOI：10.1109/TKDE.2025.3572014

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Generative recommendation systems have recently seen a surge in interest, largely due to the promising advancements in generative AI. As a competitive solution for multi-behavior sequence recommendations, much of the recent research has concentrated on predicting the next item a user will likely interact with using a generative approach. However, these methods often 1). assign multiple residual quantization layers to obtain item codes, which leads to extra storage costs of more codebooks. And 2). explicitly utilize behavior sequences leading to longer sequences, potentially increasing the training time as well as inference time compared with original sequences. In response to these challenges, we introduce the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Implicit <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Behavior <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Generative recommendation with a mixture of quantization (IMBGen) approach in this paper. Specifically, we have devised a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mixture <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quantization (MoQ) that combines the merits of both residual and parallel quantization for a more effective tokenization process. Additionally, we propose an Implicit Behavior Modeling (IBM) framework, allowing for more efficient integration of users’ behaviors into the interacted items. Finally, we conducted extensive experiments on two widely used benchmark datasets and further confirmed our findings with an online A/B test. The results consistently demonstrate the advantages of our approach over other baseline methods.

Keywords:

Recommender system

generative AI

vector quantization

LLM

implicit behavior modeling (IBM)

mixture of quantization (MoQ)

Implicit Multi-Behavior Generative Recommendation With Mixture of Quantization

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