Search Results for author:
Found 13 papers, 6 papers with code
Adam revisited: a weighted past gradients perspective
Though many algorithms, such as AMSGRAD and ADAMNC, have been proposed to fix the non-convergence issues, achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods.
Privacy Threats Against Federated Matrix Factorization
Matrix Factorization has been very successful in practical recommendation applications and e-commerce.
Vertex-reinforced Random Walk for Network Embedding
In this paper, we study the fundamental problem of random walk for network embedding.
Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
When making cloth decisions, people usually show preferences for different semantic attributes (e. g., the clothes with v-neck collar).
Ranked #1 on
Recommendation Systems
on Amazon Fashion
(nDCG@10 (500 Neg. Samples) metric, using extra
training data)
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content.
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
Learning word embeddings has received a significant amount of attention recently.
Deep Transfer Learning for Cross-domain Activity Recognition
In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR).
Cross-Domain Activity Recognition
Human Activity Recognition
+1
CityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban Data
To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e. g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city.
Topological Recurrent Neural Network for Diffusion Prediction
As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure.
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
Graph is an important data representation which appears in a wide diversity of real-world scenarios.
Active Learning for Graph Embedding
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information.
From Community Detection to Community Profiling
Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links.
From Node Embedding To Community Embedding
Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space.
