MMGRec: Multimodal Generative Recommendation with Transformer Model
<|reference_start|>MMGRec: Multimodal Generative Recommendation with Transformer Model: Multimodal recommendation aims to recommend user-preferred candidates based on her/his historically interacted items and associated multimodal information. Previous studies commonly employ an embed-and-retrieve paradigm: learning user and item representations in the same embedding space, then retrieving similar candidate items for a user via embedding inner product. However, this paradigm suffers from inference cost, interaction modeling, and false-negative issues. Toward this end, we propose a new MMGRec model to introduce a generative paradigm into multimodal recommendation. Specifically, we first devise a hierarchical quantization method Graph RQ-VAE to assign Rec-ID for each item from its multimodal and CF information. Consisting of a tuple of semantically meaningful tokens, Rec-ID serves as the unique identifier of each item. Afterward, we train a Transformer-based recommender to generate the Rec-IDs of user-preferred items based on historical interaction sequences. The generative paradigm is qualified since this model systematically predicts the tuple of tokens identifying the recommended item in an autoregressive manner. Moreover, a relation-aware self-attention mechanism is devised for the Transformer to handle non-sequential interaction sequences, which explores the element pairwise relation to replace absolute positional encoding. Extensive experiments evaluate MMGRec's effectiveness compared with state-of-the-art methods.<|reference_end|>
@article{liu2024mmgrec:,
    title={MMGRec: Multimodal Generative Recommendation with Transformer Model},
    author={Han Liu, Yinwei Wei, Xuemeng Song, Weili Guan, Yuan-Fang Li, Liqiang
  Nie},
    journal={arXiv preprint arXiv:2404.16555},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16555},
    primaryClass={cs.IR}
}