Analysis of User Behavior in Mobile Internet Using Bipartite Network

2012 ◽  
Author(s):  
Guo-Feng Zhao ◽  
Wen-Jing Lai ◽  
Chuan Xu ◽  
Hong Tang
Keyword(s):  
User Behavior ◽  
Mobile Internet ◽  
Bipartite Network

scholarly journals Characterizing User Behavior in Mobile Internet

2015 ◽  
Vol 3 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Jie Yang ◽  
Yuanyuan Qiao ◽  
Xinyu Zhang ◽  
Haiyang He ◽  
Fang Liu ◽  
...  
Keyword(s):  
User Behavior ◽  
Mobile Internet

A Location-Context Awareness Mobile Services Collaborative Recommendation Algorithm Based on User Behavior Prediction

2017 ◽  
Vol 14 (2) ◽  
pp. 45-66 ◽  
Author(s):  
Mingjun Xin ◽  
Yanhui Zhang ◽  
Shunxiang Li ◽  
Liyuan Zhou ◽  
Weimin Li
Keyword(s):  
User Behavior ◽  
Rapid Development ◽  
Mobile Internet ◽  
Location Based Services ◽  
Internet Technology ◽  
Behavior Prediction ◽  
Recommendation Algorithm ◽  
Collaborative Recommendation ◽  
Location Aware ◽  
Pattern Similarity

Nowadays, location based services (LBS) has become one of the most popular applications with the rapid development of mobile Internet technology. More and more research is focused on discovering the required services among massive information according to the personalized behavior. In this paper, a collaborative filtering (CF) recommendation algorithm is presented based on the Location-aware Hidden Markov Model (LHMM). This approach includes three main stages. First, it clusters users by making a pattern similarity calculation of their historical check-in data. Then, it establishes the location-aware transfer matrix so as to get the next most similar service. Furthermore, it integrates the generated LHMM, user's score and interest migration into the traditional CF algorithm so as to generate a final recommendation list. The LHMM-based CF algorithm mixes the geographic factors and personalized behavior and experimental results show that it outperforms the state-of-the-art algorithms on both precision and recall.

scholarly journals Personalized Recommendation for Mobile Internet Wealth Management Based on User Behavior Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiangyu Ye ◽  
Mengmeng Chen
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
User Behavior ◽  
Mobile Internet ◽  
Internet Technology ◽  
Personalized Recommendation ◽  
Transaction Data ◽  
Wealth Management ◽  
Other Information ◽  
Management Issues

Economic development has provided good opportunities for the development of securities companies. Similarly, the development of Internet technology has also brought huge opportunities and challenges to the development of securities companies. Aiming at the current wealth management issues in the era of mobile Internet, this article attempts to develop a personalized recommendation approach on the basis of users’ behavioral data analysis. We analyzed and judged the current situation of mobile Internet wealth management using personalized recommendation systems. On the basis of personalized recommendation, we use the user’s interest tags, personalized recommendation technology, and data mining technology to analyze and summarize customer transaction records. This is done through the use of preservation of customer transaction data. By understanding customers’ investment needs, risk preferences, and other information, we can segment customers and provide them with targeted products and services. As a result of the study, a flexible personalized recommendation framework is designed and validated for mobile Internet wealth management services. The effectiveness of the proposed approach is verified through testing of the developed model.

scholarly journals Positive and Unlabeled Learning for User Behavior Analysis Based on Mobile Internet Traffic Data

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 37568-37580 ◽  
Author(s):  
Ke Yu ◽  
Yue Liu ◽  
Linbo Qing ◽  
Binbin Wang ◽  
Yongqiang Cheng
Keyword(s):  
Behavior Analysis ◽  
User Behavior ◽  
Internet Traffic ◽  
Mobile Internet ◽  
Traffic Data ◽  
User Behavior Analysis ◽  
Positive And Unlabeled Learning

scholarly journals Empirical analysis on human dynamics of sharing-bicycles’ user behavior

2020 ◽  
pp. 002029402093678
Author(s):  
Kui Yu
Keyword(s):  
User Behavior ◽  
Research Area ◽  
Research Field ◽  
Mobile Internet ◽  
Time Interval ◽  
Power Law Distribution ◽  
Human Dynamics ◽  
Scale Free ◽  
Internet Application ◽  
Spatial And Temporal Characteristics

With the development of mobile communication and global positioning system navigation and positioning technology, analysis of user behavior on mobile Internet has become a hot topic in research area. Sign in sharing-bicycles’ app, find bicycle location, and selected has become a part of mobile Internet user’s daily life. Based on the data analysis of the spatial and temporal characteristics, find out sharing-bicycle’s user behavior obeys power-law distribution. In the time interval, user behavior of sharing-bicycle has strong intermittency and weak memory; the exponent of probability that K edge nodes is three by fitting the distance of sharing-bicycle’s data curve. It is verified that mobile Internet is long to scale-free networks. We conclude seven characteristics of user’s behavior of sharing-bicycle in mobile Internet application from experimental results. The analysis of sharing-bicycle’s behavior has become a complement and extension in human dynamics research field.

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