1$^{st}$ Place Solution of WWW 2025 EReL@MIR Workshop Multimodal CTR Prediction Challenge

TL;DR

This paper presents the winning solution for a multimodal CTR prediction challenge, leveraging sequential modeling and feature interaction to improve click-through rate predictions using multimodal embeddings.

Contribution

The paper introduces a simple yet effective method of integrating multimodal embeddings with user-item interaction modeling for CTR prediction.

Findings

Achieved 0.9839 AUC on the challenge dataset

Outperformed baseline models significantly

Demonstrated effectiveness of multimodal embedding integration

Abstract

The WWW 2025 EReL@MIR Workshop Multimodal CTR Prediction Challenge focuses on effectively applying multimodal embedding features to improve click-through rate (CTR) prediction in recommender systems. This technical report presents our 1$^{st}$ place winning solution for Task 2, combining sequential modeling and feature interaction learning to effectively capture user-item interactions. For multimodal information integration, we simply append the frozen multimodal embeddings to each item embedding. Experiments on the challenge dataset demonstrate the effectiveness of our method, achieving superior performance with a 0.9839 AUC on the leaderboard, much higher than the baseline model. Code and configuration are available in our GitHub repository and the checkpoint of our model can be found in HuggingFace.