From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation

• URL: http://arxiv.org/abs/2507.05715v1
• Date: Tue, 08 Jul 2025 06:58:24 GMT
• Title: From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
• Authors: Guohao Li, Li Jing, Jia Wu, Xuefei Li, Kai Zhu, Yue He,
• Abstract summary: ID features provide initial embedding but lack semantic richness.<n>They provide a unique identifier for each user and item but hinder generalization to untrained data.<n>They assist in aligning and fusing multimodal features but may lead to representation shift.
• Score: 24.73060081099998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing multimodal collaborative filtering recommendation (MCFRec) methods rely heavily on ID features and multimodal content to enhance recommendation performance. However, this paper reveals that ID features are effective but have limited benefits in multimodal collaborative filtering recommendation. Therefore, this paper systematically deconstruct the pros and cons of ID features: (i) they provide initial embedding but lack semantic richness, (ii) they provide a unique identifier for each user and item but hinder generalization to untrained data, and (iii) they assist in aligning and fusing multimodal features but may lead to representation shift. Based on these insights, this paper proposes IDFREE, an ID-free multimodal collaborative Filtering REcommEndation baseline. IDFREE replaces ID features with multimodal features and positional encodings to generate semantically meaningful ID-free embeddings. For ID-free multimodal collaborative filtering, it further proposes an adaptive similarity graph module to construct dynamic user-user and item-item graphs based on multimodal features. Then, an augmented user-item graph encoder is proposed to construct more effective user and item encoding. Finally, IDFREE achieves inter-multimodal alignment based on the contrastive learning and uses Softmax loss as recommendation loss. Basic experiments on three public datasets demonstrate that IDFREE outperforms existing ID-based MCFRec methods, achieving an average performance gain of 72.24% across standard metrics (Recall@5, 10, 20, 50 and NDCG@5, 10, 20, 50). Exploratory and extended experiments further validate our findings on the limitations of ID features in MCFRec. The code is released at https://github.com/G-H-Li/IDFREE.

Related papers

• ReID5o: Achieving Omni Multi-modal Person Re-identification in a Single Model [38.4111384634895]
  We investigate a new challenging problem called Omni Multi-modal Person Re-identification (OM-ReID)<n>We construct ORBench, the first high-quality multi-modal dataset comprising 1,000 unique identities across five modalities.<n>We also propose ReID5o, a novel multi-modal learning framework for person ReID.
  arXiv  Detail & Related papers  (2025-06-11T04:26:13Z)
• Learning Item Representations Directly from Multimodal Features for Effective Recommendation [51.49251689107541]
  multimodal recommender systems predominantly leverage Bayesian Personalized Ranking (BPR) optimization to learn item representations.<n>We propose a novel model (i.e., LIRDRec) that learns item representations directly from multimodal features to augment recommendation performance.
  arXiv  Detail & Related papers  (2025-05-08T05:42:22Z)
• Order-agnostic Identifier for Large Language Model-based Generative Recommendation [94.37662915542603]
  Items are assigned identifiers for Large Language Models (LLMs) to encode user history and generate the next item.<n>Existing approaches leverage either token-sequence identifiers, representing items as discrete token sequences, or single-token identifiers, using ID or semantic embeddings.<n>We propose SETRec, which leverages semantic tokenizers to obtain order-agnostic multi-dimensional tokens.
  arXiv  Detail & Related papers  (2025-02-15T15:25:38Z)
• Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation [4.518104756199573]
  Molar is a sequential recommendation framework that integrates multiple content modalities with ID information to capture collaborative signals effectively.<n>By seamlessly combining multimodal content with collaborative filtering insights, Molar captures both user interests and contextual semantics, leading to superior recommendation accuracy.
  arXiv  Detail & Related papers  (2024-12-24T05:23:13Z)
• MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt [60.10555128510744]
  Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities.<n>Recently, large-scale pre-trained models like CLIP have demonstrated impressive performance in traditional single-modal object ReID tasks.<n>We introduce a novel framework called MambaPro for multi-modal object ReID.
  arXiv  Detail & Related papers  (2024-12-14T06:33:53Z)
• Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation [26.737971605928358]
  We propose an ID-free MultimOdal TOken Representation scheme named MOTOR. We first employ product quantization to discretize each item's multimodal features into discrete token IDs. We then interpret the token embeddings corresponding to these token IDs as implicit item features. The resulting representations can replace the original ID embeddings and transform the original multimodal recommender into ID-free system.
  arXiv  Detail & Related papers  (2024-10-25T03:06:10Z)
• MMGRec: Multimodal Generative Recommendation with Transformer Model [81.61896141495144]
  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.
  arXiv  Detail & Related papers  (2024-04-25T12:11:27Z)
• ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation [13.338363107777438]
  We propose a novel recommendation model by incorporating ID embeddings to enhance the salient features of both content and structure. Our method is superior to state-of-the-art multimodal recommendation methods and the effectiveness of fine-grained ID embeddings.
  arXiv  Detail & Related papers  (2023-11-10T09:41:28Z)
• 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)
• 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)

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.