OA user behavior analysis with the heterogeneous information network model

2019 ◽  
Vol 516 ◽  
pp. 552-562 ◽  
Author(s):  
Lin Yang ◽  
Yilin Wang ◽  
Yun Zhou ◽  
Jiang Wang ◽  
Changjun Fan ◽  
...  
Keyword(s):  
Behavior Analysis ◽  
Network Model ◽  
User Behavior ◽  
Information Network ◽  
User Behavior Analysis ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information

HEPre: Click frequency prediction of applications based on heterogeneous information network embedding

2021 ◽  
pp. 1-16
Author(s):  
Chao Li ◽  
Yeyu Yan ◽  
Zhongying Zhao ◽  
Jun Luo ◽  
Qingtian Zeng
Keyword(s):  
Prediction Model ◽  
Mobile Applications ◽  
Mobile Application ◽  
User Behavior ◽  
Absolute Error ◽  
Research Direction ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Network Representation

Owing the continuous enrichment of mobile application resources, mobile applications carry almost all user behaviors and preferences. The analysis of user behavior regarding mobile terminals has become an important research direction. The frequency with which users click on mobile applications reflects their preferences to a certain extent. In this study, we propose a mobile application click-frequency prediction model based on heterogeneous information network representation. This model first constructs a heterogeneous information network between users’ mobile devices and mobile applications. To generate a meaningful sequence of network-embedded nodes, we perform a random walk on a specified meta-path. Finally, the prediction of users’ mobile application click frequency is completed using representation fusion and matrix factorization. Experiments show that our method outperforms other baseline methods in terms of the mean absolute error and root mean square error. Therefore, the application of a heterogeneous information network representation method to the prediction model is effective. This study is significant to the behavior research of mobile terminal users.

scholarly journals Heterogeneous information network model for equipment-standard system

2018 ◽  
Vol 490 ◽  
pp. 935-943 ◽  
Author(s):  
Liang Yin ◽  
Li-Chen Shi ◽  
Jun-Yan Zhao ◽  
Song-Yang Du ◽  
Wen-Bo Xie ◽  
...  
Keyword(s):  
Network Model ◽  
Standard System ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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