4,948 research outputs found
Deep Interest Evolution Network for Click-Through Rate Prediction
Click-through rate~(CTR) prediction, whose goal is to estimate the
probability of the user clicks, has become one of the core tasks in advertising
systems. For CTR prediction model, it is necessary to capture the latent user
interest behind the user behavior data. Besides, considering the changing of
the external environment and the internal cognition, user interest evolves over
time dynamically. There are several CTR prediction methods for interest
modeling, while most of them regard the representation of behavior as the
interest directly, and lack specially modeling for latent interest behind the
concrete behavior. Moreover, few work consider the changing trend of interest.
In this paper, we propose a novel model, named Deep Interest Evolution
Network~(DIEN), for CTR prediction. Specifically, we design interest extractor
layer to capture temporal interests from history behavior sequence. At this
layer, we introduce an auxiliary loss to supervise interest extracting at each
step. As user interests are diverse, especially in the e-commerce system, we
propose interest evolving layer to capture interest evolving process that is
relative to the target item. At interest evolving layer, attention mechanism is
embedded into the sequential structure novelly, and the effects of relative
interests are strengthened during interest evolution. In the experiments on
both public and industrial datasets, DIEN significantly outperforms the
state-of-the-art solutions. Notably, DIEN has been deployed in the display
advertisement system of Taobao, and obtained 20.7\% improvement on CTR.Comment: 9 pages. Accepted by AAAI 201
Seek for Incantations: Towards Accurate Text-to-Image Diffusion Synthesis through Prompt Engineering
The text-to-image synthesis by diffusion models has recently shown remarkable
performance in generating high-quality images. Although performs well for
simple texts, the models may get confused when faced with complex texts that
contain multiple objects or spatial relationships. To get the desired images, a
feasible way is to manually adjust the textual descriptions, i.e., narrating
the texts or adding some words, which is labor-consuming. In this paper, we
propose a framework to learn the proper textual descriptions for diffusion
models through prompt learning. By utilizing the quality guidance and the
semantic guidance derived from the pre-trained diffusion model, our method can
effectively learn the prompts to improve the matches between the input text and
the generated images. Extensive experiments and analyses have validated the
effectiveness of the proposed method
Dual Information Enhanced Multi-view Attributed Graph Clustering
Multi-view attributed graph clustering is an important approach to partition
multi-view data based on the attribute feature and adjacent matrices from
different views. Some attempts have been made in utilizing Graph Neural Network
(GNN), which have achieved promising clustering performance. Despite this, few
of them pay attention to the inherent specific information embedded in multiple
views. Meanwhile, they are incapable of recovering the latent high-level
representation from the low-level ones, greatly limiting the downstream
clustering performance. To fill these gaps, a novel Dual Information enhanced
multi-view Attributed Graph Clustering (DIAGC) method is proposed in this
paper. Specifically, the proposed method introduces the Specific Information
Reconstruction (SIR) module to disentangle the explorations of the consensus
and specific information from multiple views, which enables GCN to capture the
more essential low-level representations. Besides, the Mutual Information
Maximization (MIM) module maximizes the agreement between the latent high-level
representation and low-level ones, and enables the high-level representation to
satisfy the desired clustering structure with the help of the Self-supervised
Clustering (SC) module. Extensive experiments on several real-world benchmarks
demonstrate the effectiveness of the proposed DIAGC method compared with the
state-of-the-art baselines.Comment: 11 pages, 4 figure
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