Data Science and Engineering最新文献_第2页

Personalized Re-ranking for Recommendation with Mask Pretraining 基于掩码预训练的个性化推荐重新排序

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-09-02 DOI: 10.1007/s41019-023-00219-6

Peng Han, Silin Zhou, Jie Yu, Zichen Xu, Lisi Chen, Shuo Shang

引用次数: 1

Special Issue of DASFAA 2023 dasfaa2023特刊

2区计算机科学

Data Science and Engineering Pub Date : 2023-09-01 DOI: 10.1007/s41019-023-00231-w

Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Yingxiao Shao, Hongzhi Yin

引用次数: 0

Efficient Network Representation Learning via Cluster Similarity 基于聚类相似性的高效网络表示学习

2区计算机科学

Data Science and Engineering Pub Date : 2023-09-01 DOI: 10.1007/s41019-023-00222-x

Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Akisato Kimura, Naonori Ueda

{"title":"Efficient Network Representation Learning via Cluster Similarity","authors":"Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Akisato Kimura, Naonori Ueda","doi":"10.1007/s41019-023-00222-x","DOIUrl":"https://doi.org/10.1007/s41019-023-00222-x","url":null,"abstract":"Abstract Network representation learning is a de facto tool for graph analytics. The mainstream of the previous approaches is to factorize the proximity matrix between nodes. However, if n is the number of nodes, since the size of the proximity matrix is $$n times n$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>×</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> , it needs $$O(n^3)$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>3</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> time and $$O(n^2)$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> space to perform network representation learning; they are significantly high for large-scale graphs. This paper introduces the novel idea of using similarities between clusters instead of proximities between nodes; the proposed approach computes the representations of the clusters from similarities between clusters and computes the representations of nodes by referring to them. If l is the number of clusters, since $$l ll n$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>l</mml:mi> <mml:mo>≪</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> , we can efficiently obtain the representations of clusters from a small $$l times l$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>l</mml:mi> <mml:mo>×</mml:mo> <mml:mi>l</mml:mi> </mml:mrow> </mml:math> similarity matrix. Furthermore, since nodes in each cluster share similar structural properties, we can effectively compute the representation vectors of nodes. Experiments show that our approach can perform network representation learning more efficiently and effectively than existing approaches.","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fully Dynamic Contraction Hierarchies with Label Restrictions on Road Networks 路网上具有标签限制的完全动态收缩层次结构

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-09-01 DOI: 10.1007/s41019-023-00227-6

Zi Chen, Bo Feng, Long Yuan, Xuemin Lin, Liping Wang

引用次数: 0

Deep Learning-Based Bloom Filter for Efficient Multi-key Membership Testing 基于深度学习的高效多键隶属度测试布隆过滤器

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-09-01 DOI: 10.1007/s41019-023-00224-9

Haitian Chen, Ziwei Wang, Yunchuan Li, Ruixin Yang, Yan Zhao, Ruibo Zhou, Kai Zheng

引用次数: 0

Combining Graph Contrastive Embedding and Multi-head Cross-Attention Transfer for Cross-Domain Recommendation 结合图对比嵌入和多头交叉注意转移的跨领域推荐

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-09-01 DOI: 10.1007/s41019-023-00226-7

Shuo Xiao, Dongqing Zhu, Chaogang Tang, Zhenzhen Huang

引用次数: 0

Learning with Small Data: Subgraph Counting Queries 小数据学习:子图计数查询

2区计算机科学

Data Science and Engineering Pub Date : 2023-09-01 DOI: 10.1007/s41019-023-00223-w

Kangfei Zhao, Zongyan He, Jeffrey Xu Yu, Yu Rong

{"title":"Learning with Small Data: Subgraph Counting Queries","authors":"Kangfei Zhao, Zongyan He, Jeffrey Xu Yu, Yu Rong","doi":"10.1007/s41019-023-00223-w","DOIUrl":"https://doi.org/10.1007/s41019-023-00223-w","url":null,"abstract":"Abstract Deep Learning (DL) has been widely used in many applications, and its success is achieved with large training data. A key issue is how to provide a DL solution when there is no large training data to learn initially. In this paper, we explore a meta-learning approach for a specific problem, subgraph isomorphism counting, which is a fundamental problem in graph analysis to count the number of a given pattern graph, p , in a data graph, g , that matches p . There are various data graphs and pattern graphs. A subgraph isomorphism counting query is specified by a pair, ( g , p ). This problem is NP-hard and needs large training data to learn by DL in nature. We design a Gaussian Process (GP) model which combines Graph Neural Network with Bayesian nonparametric, and we train the GP by a meta-learning algorithm on a small set of training data. By meta-learning, we can obtain a generalized meta-model to better encode the information of data and pattern graphs and capture the prior of small tasks. With the meta-model learned, we handle a collection of pairs ( g , p ), as a task, where some pairs may be associated with the ground-truth, and some pairs are the queries to answer. There are two cases. One is there are some with ground-truth (few-shot), and one is there is none with ground-truth (zero-shot). We provide our solutions for both. In particular, for zero-shot, we propose a new data-driven approach to predict the count values. Note that zero-shot learning for our regression tasks is difficult, and there is no hands-on solution in the literature. We conducted extensive experimental studies to confirm that our approach is robust to model degeneration on small training data, and our meta-model can fast adapt to new queries by few-shot and zero-shot learning.","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136355012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation SSTP:社会和时空感知下一个兴趣点建议

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-08-30 DOI: 10.1007/s41019-023-00221-y

Junzhuang Wu, Yujing Zhang, Yuhua Li, Yixiong Zou, Rui Li, Zhenyu Zhang

引用次数: 0

A Neural Inference of User Social Interest for Item Recommendation 面向项目推荐的用户社会兴趣神经推理

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-08-29 DOI: 10.1007/s41019-023-00225-8

Junyang Chen, Ziyi Chen, Mengzhu Wang, Ge Fan, Guo Zhong, Ou Liu, Wenfeng Du, Zhenghua Xu, Zhiguo Gong

引用次数: 0

A One-Size-Fits-Three Representation Learning Framework for Patient Similarity Search 一种适合三种患者相似度搜索的学习框架

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2023-08-12 DOI: 10.1007/s41019-023-00216-9

Yefan Huang, Feng Luo, Xiaoli Wang, Zhu Di, Bo Li, Bin Luo

引用次数: 1