Papers Multimedia recommendation
“Multimedia recommendation” 태그가 달린 논문 23편 · 필터 해제
Less is More: Information Bottleneck Denoised Multimedia Recommendation
Empowered by semantic-rich content information, multimedia recommendation has emerged as a potent personalized technique. Current endeavors center around harnessing multimedia content to refine item representation or unc…
Multimedia recommendationModality-Balanced Learning for Multimedia Recommendation
Many recommender models have been proposed to investigate how to incorporate multimodal content information into traditional collaborative filtering framework effectively. The use of multimodal information is expected to…
Collaborative FilteringcounterfactualCounterfactual InferenceKnowledge Distillation+2It is Never Too Late to Mend: Separate Learning for Multimedia Recommendation
Multimedia recommendation, which incorporates various modalities (e.g., images, texts, etc.) into user or item representation to improve recommendation quality, and self-supervised learning carries multimedia recommendat…
cross-modal alignmentMultimedia recommendationSelf-Supervised LearningBeFA: A General Behavior-driven Feature Adapter for Multimedia Recommendation
Multimedia recommender systems focus on utilizing behavioral information and content information to model user preferences. Typically, it employs pre-trained feature encoders to extract content features, then fuses them …
Multimedia recommendationRecommendation SystemsMONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
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. Our study aims to exploit multimo…
Multimedia recommendationRecommendation SystemsLD4MRec: Simplifying and Powering Diffusion Model for Multimedia Recommendation
Multimedia recommendation aims to predict users' future behaviors based on observed behaviors and item content information. However, the inherent noise contained in observed behaviors easily leads to suboptimal recommend…
Multimedia recommendationRecommendation SystemsFormalizing Multimedia Recommendation through Multimodal Deep Learning
Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains. Multimodality can help tap into rich…
BenchmarkingDeep LearningMultimedia recommendationMultimodal Deep Learning+1Pareto Invariant Representation Learning for Multimedia Recommendation
Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder. However, these generic representations introduce spurious correlations that fail to …
Multimedia recommendationMulti-modal RecommendationRepresentation LearningMulti-View Graph Convolutional Network for Multimedia Recommendation
Multimedia recommendation has received much attention in recent years. It models user preferences based on both behavior information and item multimodal information. Though current GCN-based methods achieve notable succe…
Multimedia recommendationLightGT: A Light Graph Transformer for Multimedia Recommendation
Multimedia recommendation methods aim to discover the user preference on the multi-modal information to enhance the collaborative filtering (CF) based recommender system. Nevertheless, they seldom consider the impact of …
Collaborative FilteringMicrovideo RecommendationMicro-video recommendationsMultimedia recommendation+3Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation
Multimedia recommendation aims to fuse the multi-modal information of items for feature enrichment to improve the recommendation performance. However, existing methods typically introduce multi-modal information based on…
Multimedia recommendationMining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation
Multimedia content is of predominance in the modern Web era. In real scenarios, multiple modalities reveal different aspects of item attributes and usually possess different importance to user purchase decisions. However…
Multimedia recommendationMulti-Modal Self-Supervised Learning for Recommendation
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 represent…
Contrastive LearningData AugmentationMultimedia recommendationMulti-modal Recommendation+2Self-Supervised Learning for Multimedia Recommendation
Learning representations for multimedia content is critical for multimedia recommendation. Current representation learning methods roughly fall into two groups: (1) using the historical interactions to create ID embeddin…
Contrastive LearningData AugmentationMultimedia recommendationMulti-modal Recommendation+2A multimedia recommendation model based on collaborative graph
As one of the main solutions to the information overload problem, recommender systems are widely used in daily life. In the recent emerging micro-video recommendation scenario, micro-videos contain rich multimedia inform…
Graph Neural NetworkmodelMultimedia recommendationRecommendation SystemsDualGNN: Dual Graph Neural Network for Multimedia Recommendation
One of the important factors affecting micro-video recommender systems is to model the multi-modal user preference on the micro-video. Despite the remarkable performance of prior arts, they are still limited by fusing th…
Graph Neural NetworkMultimedia recommendationMulti-modal RecommendationRecommendation Systems+1GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interaction graph, edges between user and item …
Multimedia recommendationMulti-modal RecommendationLatent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Recent years have witnessed growing interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Previous studies focus on modeling user-item interacti…
Collaborative FilteringMultimedia recommendationMining Latent Structures for Multimedia Recommendation
Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommender systems. The majority of previous wo…
Collaborative FilteringMultimedia recommendationMulti-modal RecommendationMultimodal Recommendation+1ContentWise 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 …
Multimedia recommendation