‘. . . Knowledge Discovery is the most desirable end-product of computing. Finding new phenomena or enhancing our knowledge about them has a greater long-range value than optimizing production processes or inventories, and is second only to task that preserve our world and our environment. It is not surprising that it is also one of the most difficult computing challenges to do well . . .’ (Wiederhold, 1996).
The main objective of knowledge discovery in Data Mining lies in the finding of data patterns. The knowledge about the current customers can be used to predict profitable customers based on their personal information. This explorative report focuses on analysing different methods of data mining to predict profitable customers of a dating site. The second key aspect is to match individual customers based on their personal information.
The dataset analysed is derived from the customer database of Australia’s largest dating site with over 1.9 million members. The dataset contains static activity and dynamic activity. Static activity includes all personal, demographic and interest information entered by the customer at its registration. The emails sent, channels communicated and kisses sent describe the dynamic activity.
At a glance
- Grade: 1.0 (1.0 is the best grade)
- Original analysis of dating-site records from 101243 initial rows
- SAS and SQL data preparation code provided in the appendix
- Compares regression, decision trees, neural networks, clustering and SOM
- 11 sources in the bibliography
Table of Contents
1 INTRODUCTION
2 RELATED WORK
3 CONCEPTUALIZATION OF PROCESSES OF DISCOVERY
4 PRE-PROCESSING AND POST-PROCESSING
5 APPLICATION
5.1 How do users interact?
5.2 Which users are likely to pay based on dynamic data?
5.3 Which users are likely to pay based on static data?
5.4 Combine the findings
6 COMPARISON TO TRADITIONAL TOOLS
7 CONCLUSION
Objectives and Themes
This exploratory case study investigates how data mining can reveal patterns in online dating users’ interactions and help predict which customers are likely to pay for the service. It asks how static profile information and dynamic activity can be used to develop customer classification rules and inform website strategy.
- Online dating profiles and user interactions
- Static demographic and interest attributes
- Dynamic activity and stamp purchasing
- Regression, clustering and decision trees
- Data preparation and customer classification
- Prediction rules and potential website applications
Excerpt from the Book
2 RELATED WORK
Online social networks and identity representation are active research areas with input from computer sciences, statistics, sociology and psychology. Studies on psychological aspects of social identity representation examine the social implication of displaying public identities (Donath, 2004). The aim of this paper is the analysis of the interactivity between the users of an online dating website and how that influences their payment behaviour.
Toma (2008) addresses the self-presentation issue by observing the characteristics of users to establish the truth about online dating profiles. Hu and Zeng (2007), also use a framework to predict users’ identity upon their self-presentation history. While their proposed algorithm achieved high accuracy on prediction, their method is not able to clarify if the predicted traits are real or fabricated.
There are some recent academic studies on online social interaction using popular networks. Carverlee and Webb (2008) studied the characteristic of MySpace profiles based on facets of this social network. This paper has similarity to our work, however the focused was to identify elements of sociability and explain the use of language within different type of gender. The works on other social networks such as Facebook also focus on identity presentation and information sharing in student networks. Acqusiti & Gross (2006) and Tufekci (2008) also examined the disclosure behaviour on MySpace and Facebook users in correlation to privacy issues. The authors proposed a methodology for clustering and identifying similarity in user’s behaviours on YouTube data. Lerman and Jones (2006) used a small data sample from Flickr and found that the social network is used to locate new content in the site. Nowwell (2003) investigated co-authorship networks in physics to test how well different graph proximity metrics can predict future collaborations.
The paper at hand focuses on analysing the monetary aspect of an online dating website based on the user profiles. The company will benefit from the resulting prediction rules. Similar to the work of Carverlee and Webb the basis is the analysis of the user behaviour. This is extended by a more prediction-orientated analysis, not to be found before in scientistic literature in the context of dating websites. Accordingly, the website can develop a focus on the target customers and try to attract the potential customers.
Chapter Summaries
1 INTRODUCTION: Introduces the customer data and the questions concerning user interaction and payment behaviour.
2 RELATED WORK: Places the analysis alongside research on online identity, social networks and user behaviour.
3 CONCEPTUALIZATION OF PROCESSES OF DISCOVERY: Sets out the analytical workflow and considers regression, clustering, neural networks, decision trees and link analysis.
4 PRE-PROCESSING AND POST-PROCESSING: Explains how the data is imported, merged and cleaned, and how behavioural variables are grouped for analysis.
5 APPLICATION: Applies data mining methods to user interactions and payment propensity, then combines rules derived from static and dynamic variables.
6 COMPARISON TO TRADITIONAL TOOLS: Compares data mining with traditional SQL-based analysis and discusses the use of SAS.
7 CONCLUSION: Reviews the analytical approaches, their limitations and possible applications of the classification rules.
Keywords
data mining, online dating, customer behaviour, payment prediction, user interaction, static data, dynamic data, regression analysis, decision trees, cluster analysis, neural networks, SAS, SQL, classification rules
Frequently Asked Questions
What is this study about?
It examines online dating customer data to understand user interactions and explore ways to predict payment behaviour.
What are the central themes?
The study focuses on profile attributes, online interactions, stamp purchases, customer classification and the practical use of prediction rules.
What is the primary research objective?
The authors aim to identify patterns that can help distinguish customers likely to pay, using both static profile information and dynamic activity data.
What research methods are used?
The case study applies data preparation and data mining techniques including regression, clustering and decision trees, and discusses neural networks as a classification method.
What does the main analysis cover?
It explores how users interact, examines payment propensity using dynamic and static variables, and combines the resulting classification rules.
Which keywords characterize the work?
Key terms include data mining, online dating, customer behaviour, payment prediction, regression, clustering, decision trees and SQL.
What is the difference between static and dynamic data?
Static data describes profile characteristics such as demographics and interests; dynamic data records actions such as profile views, messages, channels and kisses.
How are stamps used in the analysis?
Stamp purchases represent payment activity and serve as a target for regression and customer classification.
How are the resulting rules intended to be used?
The authors discuss converting classification rules into SQL commands that could support customer targeting and other website applications.
- Arbeit zitieren
- B. Sc. Mathias Riechert (Autor:in), Xiaomin Su (Autor:in), Han Chen Hsu (Autor:in), 2010, Influence Factors For Online Dating Profit, München, GRIN Verlag, https://www.grin.com/document/171760
