Search Results for author:
Found 9 papers, 5 papers with code
CEV-LM: Controlled Edit Vector Language Model for Shaping Natural Language Generations
As large-scale language models become the standard for text generation, there is a greater need to tailor the generations to be more or less concise, targeted, and informative, depending on the audience/application.
Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems
Thus, we use the statistical characteristics of the user-item interaction matrix to identify dataset-independent representations for users and items.
Audience-Centric Natural Language Generation via Style Infusion
While existing approaches demonstrate textual style transfer with large volumes of parallel or non-parallel data, we argue that grounding style on audience-independent external factors is innately limiting for two reasons.
Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework
We present InfoMotif, a new semi-supervised, motif-regularized, learning framework over graphs.
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems.
Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks
Different strategies give rise to different social payoffs, the best performing individuals exhibit stability in their preference over the discovered strategies, which indicates the emergence of strategic behavior, and the stability of strategy preference is correlated with high payoffs.
Social and Information Networks Physics and Society
Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction
Recent years have witnessed tremendous interest in understanding and predicting information spread on social media platforms such as Twitter, Facebook, etc.
An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms
We develop a novel induced relational graph convolutional network (IR-GCN) framework to address the question.
Improving Latent User Models in Online Social Media
Modern social platforms are characterized by the presence of rich user-behavior data associated with the publication, sharing and consumption of textual content.
