Search Results for author:
Found 13 papers, 4 papers with code
paper2repo: GitHub Repository Recommendation for Academic Papers
Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\textit{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic.
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
Reinforcement Learning-based Product Delivery Frequency Control
We demonstrate statistically significant improvement in daily metrics and resource efficiency by our method in several notification applications at a scale of billions of users.
Two-Step Image Dehazing with Intra-domain and Inter-domain Adaptation
To alleviate the intra-domain gap of the synthetic domain, we propose an intra-domain adaptation to align distributions of other subsets to the optimal subset by adversarial learning.
LGA-RCNN: Loss-Guided Attention for Object Detection
Object detection is widely studied in computer vision filed.
Disentangled Contrastive Learning for Social Recommendation
In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec.
Simultaneous upper and lower bounds of American option prices with hedging via neural networks
In this paper, we introduce two methods to solve the American-style option pricing problem and its dual form at the same time using neural networks.
FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation
Item list continuation is proposed to model the overall trend of a list and predict subsequent items.
Multi-Domain Learning From Insufficient Annotations
Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains.
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
To model the compatibility between user intents and item properties, we design the user-item co-clustering module, maximizing the mutual information of co-clusters of users and items.
Dataset Condensation for Recommendation
However, applying existing approaches to condense recommendation datasets is impractical due to following challenges: (i) sampling-based methods are inadequate in addressing the long-tailed distribution problem; (ii) synthesizing-based methods are not applicable due to discreteness of interactions and large size of recommendation datasets; (iii) neither of them fail to address the specific issue in recommendation of false negative items, where items with potential user interest are incorrectly sampled as negatives owing to insufficient exposure.
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Notably, the condensation paradigm of this method is forward and free from iterative optimization on the synthesized dataset.
Discrete Semantic Tokenization for Deep CTR Prediction
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.
