Generative Recommender with End-to-End Learnable Item Tokenization

• URL: http://arxiv.org/abs/2409.05546v2
• Date: Sat, 12 Apr 2025 12:16:58 GMT
• Title: Generative Recommender with End-to-End Learnable Item Tokenization
• Authors: Enze Liu, Bowen Zheng, Cheng Ling, Lantao Hu, Han Li, Wayne Xin Zhao,
• Abstract summary: ETEGRec is a novel End-To-End Generative Recommender that unifies item tokenization and generative recommendation into a cohesive framework.<n>Built on a dual encoder-decoder architecture, ETEGRec consists of an item tokenizer and a generative recommender.<n>We develop an alternating optimization technique to ensure stable and efficient end-to-end training of the entire framework.
• Score: 51.82768744368208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative recommendation systems have gained increasing attention as an innovative approach that directly generates item identifiers for recommendation tasks. Despite their potential, a major challenge is the effective construction of item identifiers that align well with recommender systems. Current approaches often treat item tokenization and generative recommendation training as separate processes, which can lead to suboptimal performance. To overcome this issue, we introduce ETEGRec, a novel End-To-End Generative Recommender that unifies item tokenization and generative recommendation into a cohesive framework. Built on a dual encoder-decoder architecture, ETEGRec consists of an item tokenizer and a generative recommender. To enable synergistic interaction between these components, we propose a recommendation-oriented alignment strategy, which includes two key optimization objectives: sequence-item alignment and preference-semantic alignment. These objectives tightly couple the learning processes of the item tokenizer and the generative recommender, fostering mutual enhancement. Additionally, we develop an alternating optimization technique to ensure stable and efficient end-to-end training of the entire framework. Extensive experiments demonstrate the superior performance of our approach compared to traditional sequential recommendation models and existing generative recommendation baselines.

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