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Found 5 papers, 4 papers with code
On Practical Diversified Recommendation with Controllable Category Diversity Framework
These neglected non-interaction preferences are especially important for broadening user's interests in alleviating echo chamber/filter bubble effects. Therefore, in this paper, we first define diversity as two distinct definitions, i. e., user-explicit diversity (U-diversity) and user-item non-interaction diversity (N-diversity) based on user historical behaviors.
Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
Recently, a new recommendation scenario, called Trigger-Induced Recommendation (TIR), where users are able to explicitly express their instant interests via trigger items, is emerging as an essential role in many e-commerce platforms, e. g., Alibaba. com and Amazon.
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image Fusion
In this study, we show that, the structures of generative networks capture a great deal of image feature priors, and then these priors are sufficient to reconstruct high-quality fused super-resolution result using only low-resolution inputs.
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging
Multi-focus image fusion (MFIF) and super-resolution (SR) are the inverse problem of imaging model, purposes of MFIF and SR are obtaining all-in-focus and high-resolution 2D mapping of targets.
Deep Multi-Interest Network for Click-through Rate Prediction
Then the Multi-Interest Extractor Layer is applied to extract multiple user interests.
