Multimodal Representation Compression (MRC)

• Multimodal Representation Compression (MRC) is a method that compresses and fuses high-dimensional inputs like images and text while retaining essential sequential and semantic signals.
• It uses modality-specific encoders, feature fusion networks, and bottleneck layers to streamline processing in large language model-based sequential recommendation systems.
• Empirical results demonstrate that MRC reduces computational overhead and memory usage while maintaining or even improving recommendation accuracy.

Multimodal Representation Compression (MRC) refers to a class of methodologies designed to efficiently encode, reduce, and structure joint representations from multiple modalities—most commonly text and vision—while retaining information critical for downstream tasks such as sequential recommendation or LLM prompting. MRC is especially salient in multimodal LLM-based sequential recommendation frameworks, where input sequences of multimodal items (e.g., products with both images and textual descriptions) can be lengthy and feature high redundancy. Compression seeks to mitigate computational overhead, maintain discriminative power, and maximally leverage sequential signals such as order and proximity.

1. Motivation for Multimodal Representation Compression

Multimodal sequential recommendation poses unique challenges beyond standard monomodal (e.g., text-only) architectures. Sequence inputs may consist of rich, high-dimensional features—e.g., images, lengthy texts, attributes—leading to exorbitant memory and computational costs when pushed through standard LLMs or large transformer backbones. Without MRC, model throughput and inference latency are bottlenecked by the quadratic attention complexity and the redundancy in items’ content. Furthermore, as sequence length increases, attention dilution can occur, whereby early items in the sequence lose influence due to exponential decay in attention weights, hindering the ability to capture long-range dependencies that are often key for nuanced recommendation (Zhong et al., 8 Nov 2025).

MRC addresses redundancy at the representation level, allowing for more efficient sequence processing and improved utilization of position-aware mechanisms and multi-modal fusion.

2. Formal Framework and Integration with Sequential Recommendation Pipelines

Within a state-of-the-art multimodal recommendation paradigm such as Speeder (Zhong et al., 8 Nov 2025), MRC operates as the front-end of the pipeline:

• Each item in a user’s historical interaction sequence is described by a set of high-dimensional multimodal features: vision (e.g., a product image), text (e.g., title and description), and potentially structured attributes.
• MRC ingests these raw modalities and produces a single compressed multimodal embedding per item, denoted $\mathbf{e}^{mm}_j \in \mathbb{R}^d$, where $j$ indexes the item’s position in the sequence.
• The full sequential context for user $i$ becomes a set $$S_i^{mm} = \{ \mathbf{e}_1^{mm}, ..., \mathbf{e}_n^{mm} \}$$ for a history of length $n$.
• These compressed representations are subsequently used both for (a) sequential position awareness enhancement mechanisms (e.g., position prompt learning, position proxy tasks) and (b) main-task operations, such as next-item prediction or candidate ranking.

In a complete framework, MRC is coupled tightly with SPAE (Sequential Position Awareness Enhancement), ensuring that the reduction in per-item description does not strip away information necessary for modeling complex order-dependent patterns.

3. MRC Methodology: Architectures and Mechanisms

The practical realization of MRC in multimodal LLM-based recommendation systems centers around joint-encoding blocks and bottleneck networks designed to fuse modalities before compression:

• Modality-specific encoders: Each modality—image and text—is processed through backbone networks (e.g., ViT for images, BERT/LLM for text). These initial feature extractors can be frozen or fine-tuned.
• Feature fusion network: A fusion block, such as cross-modal attention or modality alignment modules, merges features into an intermediate multimodal representation.
• Compression layer: An explicit bottleneck (e.g., linear projection, low-rank matrix, or transformer bottleneck) reduces the joint embedding dimension. Typical choices are linear projections to size $d$ matching the LLM hidden state.
• Redundancy reduction: Compression may be enhanced by sparsity constraints, low-rank priors, or loss terms penalizing redundancy (although these explicit strategies are not detailed in the canonical specification for Speeder, the general practice is supported throughout the literature).

After MRC, the reduced-dimension vectors $\mathbf{e}^{mm}_j$ are suitably compact for sequence modeling with LLMs at scale on real-world item catalogs.

4. Computational and Practical Benefits

MRC directly addresses the prohibitive resource requirements of large-scale multimodal LLMs:

• Speedup: The Speeder system demonstrates a 2.5x training speed and a 4x inference speed improvement relative to prior multimodal LLM-based SR models, attributable primarily to the decrease in per-item representation size and sequence input size to the LLM (Zhong et al., 8 Nov 2025).
• Memory Efficiency: By compressing multimodal features prior to entry into the (quadratic-complexity) self-attention stack, MRC reduces not only runtime but also memory footprint, enabling deployment on commodity hardware or inference at scale.
• Maintained or Improved Accuracy: Despite aggressive reduction, downstream VHR@1 (Valid Hit Ratio at top-1) is either maintained or improved when paired with appropriate SPAE mechanisms (notably explicit position prompt learning and position proxy tasks).

5. Preservation of Sequential and Multimodal Signals through SPAE Coupling

A critical risk for any compression technique is the loss of position and modality-specific discriminative information. In practice, effective MRC must be integrated with robust position-awareness schemes:

• Absolute and Relative Position Cues: In Speeder, the outputs of MRC are augmented with learnable position prompts (PPL), guaranteeing each item retains information about its sequence index, mitigating attention dilution (Zhong et al., 8 Nov 2025).
• Order Sensitivity and Proxy Tasks: Auxiliary supervision (e.g., position proxy task queries on triplets of compressed items) teaches the LLM to reason about order, ensuring that compressed representations are not ambiguous with respect to their role in the underlying user sequence.

Thus, MRC entails not only dimension-reduction but also careful design of the downstream architecture to safeguard and accentuate sequential order and multimodal semantics.

6. Empirical Results and Practical Implications

On Amazon Automotive and Clothing & Shoes, the combination of MRC and advanced SPAE (Position Proxy Task + Position Prompt Learning) achieves:

• Up to 3–5% absolute improvement in VHR@1 compared to ablated models (“w/o MRC+SPAE”)
• No measurable increase in runtime; position prompt addition is a vector summation at embedding time and provides negligible parameter overhead (e.g., $n_{\max} \times d \approx 200$K compared to $7$B parameter LLMs).
• The approach is robust to a variety of backbone LLMs and multimodal input structures.

The practical implication is that MRC, when properly coupled with sequentially-aware enhancements, unlocks scaling of sequential recommendation to full-length multimodal histories in real commercial settings, with efficiency and accuracy not previously attainable (Zhong et al., 8 Nov 2025).

7. Future Research Directions

Potential avenues for MRC research include:

• Dynamic compression ratios: Adjusting the compression strength based on item redundancy, sequence context, or modality importance.
• Information-theoretic compression: Explicitly maximizing the mutual information between compressed representations and user intent labels.
• Joint optimization with SPAE: Tighter end-to-end coupling, possibly with contrastive or distillation losses that directly supervise both inter-modality fusion and sequential reasoning.
• Generalization to novel modalities: Extending MRC mechanisms to dynamic modalities (e.g., audio, temporal video frames) with minimal architectural changes.
• Realtime and streaming recommendation: Investigating latency-minimal MRC architectures for production pipelines ingesting new user behavior in real time, in line with the suggestion for future work in commercial deployments (Zhong et al., 8 Nov 2025).

A plausible implication is that MRC, through efficient encoding and preservation of discriminative structure, will be foundational for next-generation, multi-domain sequential recommender systems, especially under real-world resource constraints and heterogeneous interaction data.