MMGRec: Multimodal Generative Recommendation with Transformer Model
- URL: http://arxiv.org/abs/2404.16555v1
- Date: Thu, 25 Apr 2024 12:11:27 GMT
- Title: MMGRec: Multimodal Generative Recommendation with Transformer Model
- Authors: Han Liu, Yinwei Wei, Xuemeng Song, Weili Guan, Yuan-Fang Li, Liqiang Nie,
- Abstract summary: MMGRec aims to introduce a generative paradigm into multimodal recommendation.
We first devise a hierarchical quantization method Graph CF-RQVAE to assign Rec-ID for each item from its multimodal information.
We then train a Transformer-based recommender to generate the Rec-IDs of user-preferred items based on historical interaction sequences.
- Score: 81.61896141495144
- License:
- Abstract: 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.
Related papers
- Multimodal Difference Learning for Sequential Recommendation [5.243083216855681]
We argue that user interests and item relationships vary across different modalities.
We propose a novel Multimodal Learning framework for Sequential Recommendation, MDSRec.
Results on five real-world datasets demonstrate the superiority of MDSRec over state-of-the-art baselines.
arXiv Detail & Related papers (2024-12-11T05:08:19Z) - Unifying Generative and Dense Retrieval for Sequential Recommendation [37.402860622707244]
We propose LIGER, a hybrid model that combines the strengths of sequential dense retrieval and generative retrieval.
LIGER integrates sequential dense retrieval into generative retrieval, mitigating performance differences and enhancing cold-start item recommendation.
This hybrid approach provides insights into the trade-offs between these approaches and demonstrates improvements in efficiency and effectiveness for recommendation systems in small-scale benchmarks.
arXiv Detail & Related papers (2024-11-27T23:36:59Z) - Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential
Recommendations [50.03560306423678]
We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems.
Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
arXiv Detail & Related papers (2024-01-12T15:26:40Z) - MISSRec: Pre-training and Transferring Multi-modal Interest-aware
Sequence Representation for Recommendation [61.45986275328629]
We propose MISSRec, a multi-modal pre-training and transfer learning framework for sequential recommendation.
On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests.
On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation.
arXiv Detail & Related papers (2023-08-22T04:06:56Z) - Recommender Systems with Generative Retrieval [58.454606442670034]
We propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates.
To that end, we create semantically meaningful of codewords to serve as a Semantic ID for each item.
We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets.
arXiv Detail & Related papers (2023-05-08T21:48:17Z) - The Minority Matters: A Diversity-Promoting Collaborative Metric
Learning Algorithm [154.47590401735323]
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems.
This paper focuses on a challenging scenario where a user has multiple categories of interests.
We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
arXiv Detail & Related papers (2022-09-30T08:02:18Z) - Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
We formalize the recommender system as a sequential recommendation problem.
We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec.
Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
arXiv Detail & Related papers (2020-05-19T10:18:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.