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Generative Recommender with End-to-End Learnable Item Tokenization
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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. Our code is available at https://github.com/RUCAIBox/ETEGRec.
Forward citations
Cited by 6 Pith papers
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GENPLUGIN improves generative recommender systems by aligning language and ID views with contrastive learning, substituting language-view predictions for ground-truth ID tokens during training, and augmenting long-tai...
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Generating Long Semantic IDs in Parallel for Recommendation
RPG replaces autoregressive semantic ID generation with parallel multi-token prediction plus graph-constrained decoding, improving NDCG@10 by about 12.6% over generative baselines while keeping inference cost independ...
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