Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items

Chenghuan Guo; Hari Sundaram; Jiao Yang; Junting Wang; Yan Gao; Yanhui Guo

arxiv: 2507.22268 · v3 · submitted 2025-07-29 · 💻 cs.IR · cs.AI

Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items

Junting Wang , Chenghuan Guo , Jiao Yang , Yanhui Guo , Hari Sundaram , Yan Gao This is my paper

Pith reviewed 2026-05-19 01:44 UTC · model grok-4.3

classification 💻 cs.IR cs.AI

keywords multi-modal learningself-supervised learningitem recommendationsubstitutable itemscomplementary itemsrelational representationsdenoisingLLM supervision

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The pith

A multi-modal self-supervised framework learns relationship-aware item representations from noisy behaviors and metadata to infer substitutes and complements.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes MMSC to tackle noisy weak supervision from user behaviors and long-tailed sparse associations when inferring substitutable and complementary items. It integrates a multi-modal foundation model for encoding item metadata, a self-supervised denoising module for relationship-aware representations, hierarchical aggregation, and LLM-assisted supervision to reduce noise. A sympathetic reader would care because accurate substitute and complement suggestions improve recommendation systems for alternative purchases and follow-up items, especially for items with little data. The framework is tested on five real-world datasets where it shows large gains over baselines and works for cold-start cases.

Core claim

MMSC is a self-supervised multi-modal relational representation learning framework that combines a multi-modal foundation model adapted to encode item metadata and a self-supervised denoising module that learns relationship-aware representations from noisy user behaviors, unified by a hierarchical aggregation mechanism, and further uses LLM-assisted supervision to mitigate noise in behavior-derived supervision, resulting in consistent outperformance of baselines by 26.1 percent for substitutable and 39.2 percent for complementary item inference on five datasets while remaining effective for cold-start items.

What carries the argument

The MMSC framework, which unifies multi-modal metadata encoding from a foundation model, self-supervised denoising of noisy behavior signals into relationship-aware representations, and hierarchical aggregation with LLM-assisted supervision.

If this is right

MMSC outperforms baselines by 26.1 percent on substitutable item inference tasks.
MMSC outperforms baselines by 39.2 percent on complementary item inference tasks.
MMSC maintains strong performance on cold-start items that have sparse behavior associations.
The approach is validated across five real-world datasets with consistent gains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The denoising approach could transfer to other recommendation settings that rely on noisy user logs.
Combining foundation models with self-supervision might help relational inference in non-e-commerce domains such as content or recipe recommendations.
Testing the framework on streaming behavior data would reveal whether the hierarchical aggregation scales to dynamic environments.

Load-bearing premise

The assumption that LLM-assisted supervision can reliably reduce noise in behavior-derived signals without adding its own biases or errors.

What would settle it

Running the same experiments on the five datasets after removing the LLM-assisted supervision component and finding that performance drops to or below baseline levels would show the central claim does not hold.

read the original abstract

We study the problem of inferring substitutable and complementary items, which underpins applications such as alternative and follow-up purchase suggestions. Existing approaches typically learn from behavior-derived item-item associations using GNNs or leverage item content alone. However, these methods often overlook two key challenges: (i) user behaviors (e.g., co-view/co-purchase) only provide noisy weak supervision, and (ii) behavior signals are long-tailed, leaving many items with sparse associations. We propose MMSC, a self-supervised multi-modal relational representation learning framework that combines a multi-modal foundation model adapted to encode item metadata and a self-supervised denoising module that learns relationship-aware representations from noisy user behaviors, unified by a hierarchical aggregation mechanism. We further use LLM-assisted supervision to mitigate noise in behavior-derived supervision during training. Experiments on five real-world datasets show that MMSC consistently outperforms existing baselines by 26.1% for substitutable and 39.2% for complementary item inference, while remaining effective for cold-start items. We share our code for reproducibility.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The abstract describes a multi-modal self-supervised approach to item relation inference that addresses noise and sparsity but supplies insufficient experimental details to back the performance claims.

read the letter

The abstract gives a high-level view of MMSC, a framework that encodes item metadata with a multi-modal foundation model, denoises noisy user behavior signals through self-supervision, and applies LLM-assisted supervision to reduce noise further. Hierarchical aggregation combines these pieces. The authors report consistent outperformance of 26.1% on substitutable items and 39.2% on complementary items across five real-world datasets, with added strength on cold-start cases. This specific assembly of multi-modal encoding, denoising, and LLM supervision for the substitutable and complementary task counts as the fresh element. Earlier work either builds graphs from behavior data or uses content in isolation, but here the focus is on cleaning the weak signals and handling sparsity. The soft spots come from missing information. The abstract states the gains without describing the datasets, the baseline methods, statistical tests, or how the LLM supervision is produced and checked for errors. This leaves open whether the denoising module truly drives the results or if other elements contribute. The claim that LLM assistance mitigates noise without new biases cannot be checked from the given text. Researchers in recommender systems for e-commerce would get the most from this. Anyone dealing with relational learning from mixed behavior and content signals might borrow from the overall structure. I recommend sending the full paper for peer review. The core problems it targets are common in practice, and the design choices merit closer examination even if the abstract leaves several questions unanswered.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes MMSC, a self-supervised multi-modal relational representation learning framework for inferring substitutable and complementary items. It integrates a multi-modal foundation model to encode item metadata with a self-supervised denoising module that learns relationship-aware representations from noisy user behaviors, unified via hierarchical aggregation. LLM-assisted supervision is used to mitigate noise in behavior-derived signals. Experiments on five real-world datasets demonstrate that MMSC outperforms baselines by 26.1% for substitutable and 39.2% for complementary item inference, and is effective for cold-start items. The code is shared for reproducibility.

Significance. If the reported improvements hold under rigorous validation, this work would be significant for recommendation systems by addressing the challenges of noisy weak supervision from user behaviors and long-tailed item associations through multi-modal and LLM-assisted techniques. The emphasis on reproducibility by sharing code is a positive aspect that facilitates further research and verification.

major comments (2)

Abstract: The abstract claims specific percentage improvements (26.1% for substitutable and 39.2% for complementary items) but provides no details on the experimental methodology, baseline implementations, evaluation metrics, statistical tests, or characteristics of the five real-world datasets. This absence makes it impossible to assess whether the data supports the central claims of consistent outperformance.
Abstract: The key component of LLM-assisted supervision for mitigating noise in behavior-derived supervision is mentioned without any description of the generation procedure, validation steps, or measures to avoid introducing biases or errors from the LLM. Since this is central to the self-supervised denoising module, the lack of specifics undermines the ability to evaluate the framework's effectiveness.

minor comments (1)

Abstract: The abstract could provide a brief mention of the specific multi-modal foundation model adapted or the nature of the hierarchical aggregation mechanism to enhance clarity for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for major revision. We have revised the abstract to provide additional context on the experimental claims and the LLM-assisted supervision component. Our point-by-point responses follow.

read point-by-point responses

Referee: Abstract: The abstract claims specific percentage improvements (26.1% for substitutable and 39.2% for complementary items) but provides no details on the experimental methodology, baseline implementations, evaluation metrics, statistical tests, or characteristics of the five real-world datasets. This absence makes it impossible to assess whether the data supports the central claims of consistent outperformance.

Authors: We agree that the abstract's brevity limits the inclusion of full methodological details, which are standardly placed in the main body. The manuscript describes the five real-world datasets, baseline implementations, evaluation metrics, and statistical tests in the Experiments section. The reported percentages reflect average relative improvements over the strongest baseline across datasets. To address the concern, we have revised the abstract to briefly reference the use of standard ranking metrics on diverse e-commerce datasets and to note that full experimental details and significance testing appear in the paper. revision: yes
Referee: Abstract: The key component of LLM-assisted supervision for mitigating noise in behavior-derived supervision is mentioned without any description of the generation procedure, validation steps, or measures to avoid introducing biases or errors from the LLM. Since this is central to the self-supervised denoising module, the lack of specifics undermines the ability to evaluate the framework's effectiveness.

Authors: We acknowledge that the abstract provides only a high-level mention of LLM-assisted supervision. The full manuscript details the generation procedure, validation, and bias-mitigation steps in the Methods section. We have revised the abstract to include a concise description of how LLM-generated signals are used to denoise behavior-derived supervision, thereby clarifying its role in the framework while respecting length constraints. revision: yes

Circularity Check

0 steps flagged

No significant circularity in MMSC proposal or claims

full rationale

The abstract describes a proposed self-supervised framework MMSC that integrates a multi-modal foundation model for item metadata encoding, a denoising module for relationship-aware representations from noisy behaviors, hierarchical aggregation, and LLM-assisted supervision to mitigate noise. Performance gains (26.1% substitutable, 39.2% complementary) are presented as empirical results from experiments on five real-world datasets. No equations, derivation steps, fitted parameters renamed as predictions, or self-citations appear in the provided abstract. The approach relies on standard self-supervised learning and external LLM signals rather than any self-definitional or fitted-input reduction to its own inputs by construction. The central claims remain independent of the listed circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the effectiveness of adapting multi-modal models and using self-supervised methods to handle noisy data, which are standard but application-specific assumptions in recommendation systems.

axioms (2)

domain assumption User behavior signals such as co-view and co-purchase provide noisy weak supervision for item relationships.
Explicitly stated as one of the key challenges in the abstract.
domain assumption Behavior signals are long-tailed, leaving many items with sparse associations.
Stated as a challenge in the abstract.

pith-pipeline@v0.9.0 · 5697 in / 1336 out tokens · 69356 ms · 2026-05-19T01:44:08.281573+00:00 · methodology

discussion (0)

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self-supervised denoising module that learns relationship-aware representations from noisy user behaviors, unified by a hierarchical aggregation mechanism... LLM-assisted supervision

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