Using Behavioral Data Mining to Produce Friend Recommendations in a Social Bookmarking System

Season ticket holders are a vital source of revenue for professional teams, but retention remains a perennial issue. Prior research has focused on broad variables, such as relationship tenure, game attendance frequency, and renewal intention, and has generally been limited to survey data with its attenuate problems. To advance this important research agenda, the present study analyzes team-supplied behavioral data to investigate and predict retention as a loyalty outcome for a single professional team over a 3-year period. Specifically, the authors embrace a broad range of loyalty measures and team performance to predict retention and employ novel data mining techniques to improve predictive accuracy.

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Career Choice Prediction Based on Campus Big Data—Mining the Potential Behavior of College Students

Applied Sciences ◽

10.3390/app10082841 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2841

Author(s):

Min Nie ◽

Zhaohui Xiong ◽

Ruiyang Zhong ◽

Wei Deng ◽

Guowu Yang

Keyword(s):

College Students ◽

Data Mining ◽

Career Choice ◽

Career Choices ◽

State Of The Art ◽

Behavioral Data ◽

Professional Career ◽

The Past ◽

Big Data Mining ◽

Life Planning

Career choice has a pivotal role in college students’ life planning. In the past, professional career appraisers used questionnaires or diagnoses to quantify the factors potentially influencing career choices. However, due to the complexity of each person’s goals and ideas, it is difficult to properly forecast their career choices. Recent evidence suggests that we could use students’ behavioral data to predict their career choices. Based on the simple premise that the most remarkable characteristics of classes are reflected by the main samples of a category, we propose a model called the Approach Cluster Centers Based On XGBOOST (ACCBOX) model to predict students’ career choices. The experimental results of predicting students’ career choices clearly demonstrate the superiority of our method compared to the existing state-of-the-art techniques by evaluating on 13 M behavioral data of over four thousand students.

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Detecting Trends in Social Bookmarking Systems

International Journal of Data Warehousing and Mining ◽

10.4018/jdwm.2010090803 ◽

2010 ◽

Vol 6 (1) ◽

pp. 38-57 ◽

Cited By ~ 9

Author(s):

Robert Wetzker ◽

Carsten Zimmermann ◽

Christian Bauckhage

Keyword(s):

Data Mining ◽

Generative Model ◽

Trend Detection ◽

Future Research ◽

Social Bookmarking ◽

Smoothing Techniques ◽

Web Intelligence ◽

Major Interest ◽

The Impact ◽

Probabilistic Generative Model

The authors present and evaluate an approach to trend detection in social bookmarking systems using a probabilistic generative model in combination with smoothing techniques. Social bookmarking systems are gaining major interest among researchers in the areas of data mining and Web intelligence, since they provide a large amount of user-generated annotations and reflect the interest of millions of people. Based on a vast corpus of approximately 150 million bookmarks found at del. icio.us, the authors analyze bookmarking and tagging patterns and discuss evidence that social bookmarking systems are vulnerable to spamming. They present a method to limit the impact of spam on a trend detector and provide conclusions as well as directions for future research.

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Detecting Trends in Social Bookmarking Systems

Exploring Advances in Interdisciplinary Data Mining and Analytics ◽

10.4018/978-1-61350-474-1.ch003 ◽

2011 ◽

pp. 34-51 ◽

Cited By ~ 1

Author(s):

Robert Wetzker ◽

Carsten Zimmermann ◽

Christian Bauckhage

Keyword(s):

Data Mining ◽

Generative Model ◽

Trend Detection ◽

Future Research ◽

Social Bookmarking ◽

Smoothing Techniques ◽

Web Intelligence ◽

Major Interest ◽

The Impact ◽

Probabilistic Generative Model

The authors present and evaluate an approach to trend detection in social bookmarking systems using a probabilistic generative model in combination with smoothing techniques. Social bookmarking systems are gaining major interest among researchers in the areas of data mining and Web intelligence, since they provide a large amount of user-generated annotations and reflect the interest of millions of people. Based on a vast corpus of approximately 150 million bookmarks found at del.icio.us, the authors analyze bookmarking and tagging patterns and discuss evidence that social bookmarking systems are vulnerable to spamming. They present a method to limit the impact of spam on a trend detector and provide conclusions as well as directions for future research.

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