Adaptive personalization using social networks

Tuck Siong Chung (), Michel Wedel () and Roland T. Rust ()
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Tuck Siong Chung: Nanyang Technological University
Michel Wedel: University of Maryland
Roland T. Rust: University of Maryland

Journal of the Academy of Marketing Science, 2016, vol. 44, issue 1, No 5, 66-87

Abstract: 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.

Keywords: Personalization; Social networks; News; Bayes classifier; Recommendation systems; Mobile commerce; Smart phones; Service marketing (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (48)

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DOI: 10.1007/s11747-015-0441-x

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