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Journal of the Academy of Marketing Science
Published in: Journal of the Academy of Marketing Science 1/2016

01-01-2016 | Original Empirical Research

Adaptive personalization using social networks

Authors: Tuck Siong Chung, Michel Wedel, Roland T. Rust

Published in: Journal of the Academy of Marketing Science | Issue 1/2016

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Abstract

This research provides insights into the following questions regarding the effectiveness of mobile adaptive personalization systems: (1) to what extent can adaptive personalization produce a better service/product over time? (2) does adaptive personalization work better than self-customization? (3) does the use of the customer’s social network result in better personalization? To answer these questions, we develop and implement an adaptive personalization system for personalizing mobile news based on recording and analyzing customers’ behavior, plus information from their social network. The system learns from an individual’s reading history, automatically discovers new material as a result of shared interests in the user’s social network, and adapts the news feeds shown to the user. Field studies show that (1) repeatedly adapting to the customer’s observed behavior improves personalization performance; (2) personalizing automatically, using a personalization algorithm, results in better performance than allowing the customer to self-customize; and (3) using the customer’s social network for personalization results in further improvement. We conclude that mobile automated adaptive personalization systems that take advantage of social networks may be a promising approach to making personalization more effective.
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Appendix
Available only for authorised users
Footnotes
1
We also tested a Bayesian Logistic Regression model. That model, although seemingly more sophisticated than the Naïve Bayes approach, and a better performer in in-sample testing, actually performed substantially worse in out-of-sample tests, suggesting that the added complexity of the Bayesian Logistic Regression model resulted in over-fitting. Thus, we focus on the Naïve Bayes algorithm for the remainder of this paper.
 
2
Although the use of these terms may seem non-standard to a marketing audience, we retain them to maintain consistency with the classification literature.
 
3
A technical appendix describing the simulation is available from the authors.
 
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Metadata
Title
Adaptive personalization using social networks
Authors
Tuck Siong Chung
Michel Wedel
Roland T. Rust
Publication date
01-01-2016
Publisher
Springer US
Published in
Journal of the Academy of Marketing Science / Issue 1/2016
Print ISSN: 0092-0703
Electronic ISSN: 1552-7824
DOI
https://doi.org/10.1007/s11747-015-0441-x

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