A new SOM-based method for profile generation: Theory and an application in direct marketing

Seret, Alex; Verbraken, Thomas; Versailles, Sébastien; Baesens, Bart

doi:10.1016/j.ejor.2012.01.044

Cited by 25 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Interpreting complex data by visual inspection is something that has proven its value in marketing research. (Seret, Verbraken, Versailles, & Baesens, 2012;Azcarraga, Hsieh, Pan, & Setiono, 2005) present a methodology based on selforganising maps (SOM) that allows marketeers to get a 2D visual representation of their whole user base.…”

Section: Related Workmentioning

confidence: 99%

Understanding Recommendation Quality Using Embeddings

et al. 2017

Self Cite

View full text Add to dashboard Cite

Current methods of evaluating the quality of recommender systems are based on averages of metrics such as the average normalized discounted cumulative gain, average diversity and average reciprocity. Averages of metrics give a good sense of the overall quality of the recommendations, but not of how their quality is distributed with respect to the recommendation system's users or items. This paper presents a visual method, based on embedding a high dimensional content feature-space into a 2D image, that is capable of providing insights in which users are receiving high quality recommendations and how biased recommendation quality is with respect to different types of users. Through a proof of concept in the domain of job recommendation we show that our method allows business people to come to relevant answers to the question "For which of my users does my recommender system work well/poorly?".

show abstract

Section: Related Workmentioning

confidence: 99%

Understanding Recommendation Quality Using Embeddings

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…The clustering approach used in this work is based on a two-step clustering strategy discussed and applied in works as Louis et al (2013), Seret et al (2012), Azcarraga et al (2005) and Vesanto and Alhoniemi (2000). The rst step of this strategy consists of reducing the number of data points by training a self-organizing map (SOM) with a high number (lower than the number of input data points while being signicantly higher than the expected number of clusters) of neurons (see Kohonen, 1995, for more details).…”

Section: The Two-step Clustering Approachmentioning

confidence: 99%

Identifying next relevant variables for segmentation by using feature selection approaches

Seret

Maldonado

Baesens

2015

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

Data mining techniques are widely used by researchers and companies in order to solve problems in a myriad of domains. While these techniques are being adopted and used in daily activities, new operational challenges are encountered concerning the steps following this adoption. In this paper, the problem of updating and improving an existing clustering model by adding relevant new variables is studied. A relevant variable is here dened as a feature which is highly correlated with the current structure of the data, since our main goal is to improve the model by adding new information to the current segmentation, but without modifying it signicantly. For this purpose, a general framework is proposed, and subsequently applied in a real business context involving an event organizer facing this problem. Based on extensive experiments based on real data, the performance of the proposed approach is compared to existing methods using dierent evaluation metrics, leading to the conclusion that the proposed technique is performing better for this specic problem.

show abstract

“…2013), psychographic segmentation (Mostafa 2009), direct marketing (Curry et al . 2003;Seret et al . 2012), product demand forecasting (Lu & Wang 2010), customer retention (Chu et al 2007), customer behaviour (Fessant et al 2008) and product positioning (Hadjinicola et al 2013) .…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Self-organising Maps and Principal Components Analysis

Das

Chattopadhyay

Gupta

2016

International Journal of Market Research

View full text Add to dashboard Cite

This paper attempts to compare self-organising maps (SOM) and principal components analysis (CPA) by applying them to the marketing construct ‘retail store personality’. Data were collected for the retail store personality construct via a validated scale from previous studies that had used the mall intercept technique. A total of 367 people responded, of whom 353 were found to be valid for data analysis. Data were analysed using CPA and SOM; both methods gave comparable clustering results, although the results for SOM were quite conclusive. In addition, we found that SOM complemented PCA by providing visual clustering results far superior to those of PCA. SOM can be used to further analyse PCA data using visual clustering features; both could be used in tandem. Although SOM have been used in a number of studies in marketing, this is the first paper to compare PCA and SOM on terms of application to the marketing construct ‘retail store personality’.

show abstract

A new SOM-based method for profile generation: Theory and an application in direct marketing

Cited by 25 publications

References 16 publications

Understanding Recommendation Quality Using Embeddings

Understanding Recommendation Quality Using Embeddings

Identifying next relevant variables for segmentation by using feature selection approaches

A Comparison of Self-organising Maps and Principal Components Analysis

Contact Info

Product

Resources

About