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Found 14 papers, 2 papers with code
Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
To our knowledge, this is the first work to explicitly model item popularity in recommendation systems.
Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference
Multi-shot CRS is designed to make recommendations multiple times until the user either accepts the recommendation or leaves at the end of their patience.
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
To model complex reservoir dynamics at both local and global scale, HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure.
IA-GCN: Interactive Graph Convolutional Network for Recommendation
In this work, we address this problem by building bilateral interactive guidance between each user-item pair and proposing a new model named IA-GCN (short for InterActive GCN).
Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
In this work, we propose a new initialization scheme for user and item embeddings called Laplacian Eigenmaps with Popularity-based Regularization for Isolated Data (LEPORID).
Neural Radiance Flow for 4D View Synthesis and Video Processing
We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images.
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
Key to our approach is to exploit GANs as a multi-view data generator to train an inverse graphics network using an off-the-shelf differentiable renderer, and the trained inverse graphics network as a teacher to disentangle the GAN's latent code into interpretable 3D properties.
Learning Hierarchical Review Graph Representations for Recommendation
Therefore, they may not be effective in capturing the global dependency between words, and tend to be easily biased by noise review information.
Towards a Soft Faceted Browsing Scheme for Information Access
Faceted browsing is a commonly supported feature of user interfaces for access to information.
Explicit Pairwise Word Interaction Modeling Improves Pretrained Transformers for English Semantic Similarity Tasks
In this paper, we hypothesize that introducing an explicit, constrained pairwise word interaction mechanism to pretrained language models improves their effectiveness on semantic similarity tasks.
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years.
Towards Physically Safe Reinforcement Learning under Supervision
A weighted average of the supervisor and learned policies is used during trials, with a heavier weight initially on the supervisor, in order to allow safe and useful physical trials while the learned policy is still ineffective.
Reductive Clustering: An Efficient Linear-time Graph-based Divisive Cluster Analysis Approach
First a subset of vertices of the graph are selected as representatives to build a concise graph.
