Multimedia recommendation
10 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Multi-Modal Self-Supervised Learning for Recommendation
hkuds/mmssl
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The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (eg, visual, textual and acoustic) into the latent user representations.
Multi-View Graph Convolutional Network for Multimedia Recommendation
enoche/mmrec
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Meanwhile, a behavior-aware fuser is designed to comprehensively model user preferences by adaptively learning the relative importance of different modality features.
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
weiyinwei/mmgcn
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ACM International Conference on Multimedia 2019
Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities.
ContentWise Impressions: An Industrial Dataset with Impressions Included
In this article, we introduce the ContentWise Impressions dataset, a collection of implicit interactions and impressions of movies and TV series from an Over-The-Top media service, which delivers its media contents over the Internet.
Mining Latent Structures for Multimedia Recommendation
To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs.
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
cripac-dig/micro
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Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships. To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity).
GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
weiyinwei/grcn
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Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks.
LightGT: A Light Graph Transformer for Multimedia Recommendation
Liuwq-bit/LightGT
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SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023
Considering its challenges in effectiveness and efficiency, we propose a novel Transformer-based recommendation model, termed as Light Graph Transformer model (LightGT).
Formalizing Multimedia Recommendation through Multimodal Deep Learning
sisinflab/formal-multimod-rec
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Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains.
MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
kimyungi/monet
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In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together.
