What question did this study set out to answer?

Develop a multimodal video recommendation framework that jointly models cross-modal semantic divergence and synergy while resolving representation inconsistencies in grid-based platforms.

August 16, 2026ACM Transactions on Information Systems

Exploring the Synergetic and Divergent Potentials of Multimodal Semantics for Feature Fusion-based Video Recommendation

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Authors

ZCZiyi Cao RLRui Liu RSRui Sun

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Overview

Experimental evaluation demonstrates superior recommendation accuracy on video benchmark datasets, highlighting the benefit of modeling both semantic synergy and divergence.

Key Points

Develop a multimodal video recommendation framework that jointly models cross-modal semantic divergence and synergy while resolving representation inconsistencies in grid-based platforms.
Designed the ESDvr framework using Mixture of Gaussian Blur Gating (MoGG) to capture semantic divergence between cover images and text alongside synergistic features.
Integrated a learnable threshold mechanism to simulate user hover behavior and utilized a contrastive loss to align multimodal representations with user-item interaction histories.
Evaluated performance on the MicroLens and MovieLens benchmark datasets against state-of-the-art recommendation models.
Outperformed state-of-the-art baselines on both benchmark datasets.
Achieved relative improvements of 10.15% in Recall@5 and 10.22% in NDCG@10.

Cite This Study

Cao et al. (2026) studied this question.

synapsesocial.com/papers/6a8179abf2fb91fc834acf5e https://doi.org/10.1145/3839230

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August 16, 2026ACM Transactions on Information Systems

Exploring the Synergetic and Divergent Potentials of Multimodal Semantics for Feature Fusion-based Video Recommendation

View Full Paper

Ask AI

Bookmark

Authors

ZCZiyi Cao RLRui Liu RSRui Sun

Discussion

Member takes

Overview

Experimental evaluation demonstrates superior recommendation accuracy on video benchmark datasets, highlighting the benefit of modeling both semantic synergy and divergence.

Key Points

Develop a multimodal video recommendation framework that jointly models cross-modal semantic divergence and synergy while resolving representation inconsistencies in grid-based platforms.
Designed the ESDvr framework using Mixture of Gaussian Blur Gating (MoGG) to capture semantic divergence between cover images and text alongside synergistic features.
Integrated a learnable threshold mechanism to simulate user hover behavior and utilized a contrastive loss to align multimodal representations with user-item interaction histories.
Evaluated performance on the MicroLens and MovieLens benchmark datasets against state-of-the-art recommendation models.
Outperformed state-of-the-art baselines on both benchmark datasets.
Achieved relative improvements of 10.15% in Recall@5 and 10.22% in NDCG@10.

Cite This Study

Cao et al. (2026) studied this question.

synapsesocial.com/papers/6a8179abf2fb91fc834acf5e https://doi.org/10.1145/3839230

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