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Personalized Image Generation with Large Multimodal Models

Autor: Xu, Yiyan, Wang, Wenjie, Zhang, Yang, Biao, Tang, Yan, Peng, Feng, Fuli, He, Xiangnan

Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload. However, these systems merely filter existing content and are constrained by its limited diversity, making it difficu

Externí odkaz: http://arxiv.org/abs/2410.14170

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Diffusion Models for Generative Outfit Recommendation

Autor: Xu, Yiyan, Wang, Wenjie, Feng, Fuli, Ma, Yunshan, Zhang, Jizhi, He, Xiangnan

Outfit Recommendation (OR) in the fashion domain has evolved through two stages: Pre-defined Outfit Recommendation and Personalized Outfit Composition. However, both stages are constrained by existing fashion products, limiting their effectiveness in

Externí odkaz: http://arxiv.org/abs/2402.17279

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Denoising Diffusion Recommender Model

Autor: Zhao, Jujia, Wang, Wenjie, Xu, Yiyan, Sun, Teng, Feng, Fuli, Chua, Tat-Seng

Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the noise issues from data cleaning perspective such as data resampling and reweighting, but they are constrained by heuristic assumptions. Another denoising avenu

Externí odkaz: http://arxiv.org/abs/2401.06982

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On some rigidity theorems of Q-curvature

Autor: Xu, Yiyan, Zhang, Shihong

In this paper, we investigate the rigidity of Q-curvature. Specifically, we consider a closed, oriented $n$-dimensional ($n\geq6$) Riemannian manifold $(M,g)$ and prove the following results under the condition $\int_{M} \nabla R\cdot\nabla \mathrm{Q

Externí odkaz: http://arxiv.org/abs/2308.02777

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Diffusion Recommender Model

Autor: Wang, Wenjie, Xu, Yiyan, Feng, Fuli, Lin, Xinyu, He, Xiangnan, Chua, Tat-Seng

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, these generative models suffer from intrinsic limitations such as

Externí odkaz: http://arxiv.org/abs/2304.04971

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Mitigating Spurious Correlations for Self-supervised Recommendation

Autor: Lin, Xinyu, Xu, Yiyan, Wang, Wenjie, Zhang, Yang, Feng, Fuli

Publikováno v: Machine Intelligence Research vol. 20, no. 6, pp. 263-275, 2023

Recent years have witnessed the great success of self-supervised learning (SSL) in recommendation systems. However, SSL recommender models are likely to suffer from spurious correlations, leading to poor generalization. To mitigate spurious correlati

Externí odkaz: http://arxiv.org/abs/2212.04282

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