Learning Item Representations Directly from Multimodal Features for Effective Recommendation

• URL: http://arxiv.org/abs/2505.04960v1
• Date: Thu, 08 May 2025 05:42:22 GMT
• Title: Learning Item Representations Directly from Multimodal Features for Effective Recommendation
• Authors: Xin Zhou, Xiaoxiong Zhang, Dusit Niyato, Zhiqi Shen,
• Abstract summary: 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.
• Score: 51.49251689107541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional multimodal recommender systems predominantly leverage Bayesian Personalized Ranking (BPR) optimization to learn item representations by amalgamating item identity (ID) embeddings with multimodal features. Nevertheless, our empirical and theoretical findings unequivocally demonstrate a pronounced optimization gradient bias in favor of acquiring representations from multimodal features over item ID embeddings. As a consequence, item ID embeddings frequently exhibit suboptimal characteristics despite the convergence of multimodal feature parameters. Given the rich informational content inherent in multimodal features, in this paper, we propose a novel model (i.e., LIRDRec) that learns item representations directly from these features to augment recommendation performance. Recognizing that features derived from each modality may capture disparate yet correlated aspects of items, we propose a multimodal transformation mechanism, integrated with modality-specific encoders, to effectively fuse features from all modalities. Moreover, to differentiate the influence of diverse modality types, we devise a progressive weight copying fusion module within LIRDRec. This module incrementally learns the weight assigned to each modality in synthesizing the final user or item representations. Finally, we utilize the powerful visual understanding of Multimodal Large Language Models (MLLMs) to convert the item images into texts and extract semantics embeddings upon the texts via LLMs. Empirical evaluations conducted on five real-world datasets validate the superiority of our approach relative to competing baselines. It is worth noting the proposed model, equipped with embeddings extracted from MLLMs and LLMs, can further improve the recommendation accuracy of NDCG@20 by an average of 4.21% compared to the original embeddings.

Related papers

• Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation [9.37169920239321]
  Multimodal Recommender Systems aim to improve recommendation accuracy by integrating heterogeneous content.<n>While effective, it remains unclear whether their gains stem from true multimodal understanding or increased model complexity.<n>This work investigates the role of multimodal item embeddings, emphasizing the semantic informativeness of the representations.
  arXiv  Detail & Related papers  (2025-08-06T15:53:58Z)
• BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation [55.486872677160015]
  We reformulate multi-modal semantic segmentation as a mask-level classification task.<n>We propose BiXFormer, which integrates Unified Modality Matching (UMM) and Cross Modality Alignment (CMA)<n> Experiments on both synthetic and real-world multi-modal benchmarks demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2025-06-04T08:04:58Z)
• IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification [60.38841251693781]
  We propose a novel framework to generate robust multi-modal object ReIDs.<n>Our framework uses Modal Prefixes and InverseNet to integrate multi-modal information with semantic guidance from inverted text.<n>Experiments on three multi-modal object ReID benchmarks demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2025-03-13T13:00:31Z)
• Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts [56.7225771305861]
  This paper introduces Multi-Modal Retrieval-Augmented Generation (M$2$RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models.<n>The benchmark comprises four tasks: image captioning, multi-modal question answering, multi-modal fact verification, and image reranking.<n>To enhance the context utilization capabilities of MLLMs, we also introduce Multi-Modal Retrieval-Augmented Instruction Tuning (MM-RAIT)
  arXiv  Detail & Related papers  (2025-02-24T16:25:25Z)
• 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)
• Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations [13.878297630442674]
  This paper introduces a novel framework for multi-behavior recommendations, leveraging the fusion of triple-modality.<n>Our proposed model called Triple Modality Fusion (TMF) utilizes the power of large language models (LLMs) to align and integrate these three modalities.<n>Extensive experiments demonstrate the effectiveness of our approach in improving recommendation accuracy.
  arXiv  Detail & Related papers  (2024-10-16T04:44:15Z)
• M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning [90.75075886543404]
  Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains. In this work, we introduce a novel Multimodal Prompt Tuning (M$2$PT) approach for efficient instruction tuning of MLLMs.
  arXiv  Detail & Related papers  (2024-09-24T01:40:24Z)
• NoteLLM-2: Multimodal Large Representation Models for Recommendation [71.87790090964734]
  Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks.<n>Their potential in multimodal representation, particularly for item-to-item (I2I) recommendations, remains underexplored.<n>We propose an end-to-end fine-tuning method that customizes the integration of any existing LLMs and vision encoders for efficient multimodal representation.
  arXiv  Detail & Related papers  (2024-05-27T03:24:01Z)
• MM-GEF: Multi-modal representation meet collaborative filtering [43.88159639990081]
  We propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals.
  arXiv  Detail & Related papers  (2023-08-14T15:47:36Z)
• Mining Latent Structures for Multimedia Recommendation [46.70109406399858]
  We propose a LATent sTructure mining method for multImodal reCommEndation, which we term LATTICE for brevity. We learn item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs. Based on the learned latent graphs, we perform graph convolutions to explicitly inject high-order item affinities into item representations.
  arXiv  Detail & Related papers  (2021-04-19T03:50:24Z)

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.