Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Zheng, Vincent W."'
PathSim is a widely used meta-path-based similarity in heterogeneous information networks. Numerous applications rely on the computation of PathSim, including similarity search and clustering. Computing PathSim scores on large graphs is computational
Externí odkaz:
http://arxiv.org/abs/2109.01549
Publikováno v:
Front. Comput. Sci. 14, 145309 (2020)
Adaptive learning rate methods have been successfully applied in many fields, especially in training deep neural networks. Recent results have shown that adaptive methods with exponential increasing weights on squared past gradients (i.e., ADAM, RMSP
Externí odkaz:
http://arxiv.org/abs/2101.00238
In modern social media platforms, an effective content recommendation should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. To address the limitations of existing methods for social
Externí odkaz:
http://arxiv.org/abs/2012.04945
Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect sufficient data to build performant recommender systems for a single comp
Externí odkaz:
http://arxiv.org/abs/2007.01587
In this paper, we study the fundamental problem of random walk for network embedding. We propose to use non-Markovian random walk, variants of vertex-reinforced random walk (VRRW), to fully use the history of a random walk path. To solve the getting
Externí odkaz:
http://arxiv.org/abs/2002.04497
In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck
Externí odkaz:
http://arxiv.org/abs/1905.12862
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. In this paper, we propose a model called Social Explorative
Externí odkaz:
http://arxiv.org/abs/1905.11900
Publikováno v:
D17-1312, 2017, 2898-2904
Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectivene
Externí odkaz:
http://arxiv.org/abs/1902.00184
Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the
Externí odkaz:
http://arxiv.org/abs/1807.07963
In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an inactive node to be activated next in a cascade. Despite the success
Externí odkaz:
http://arxiv.org/abs/1711.10162