Multi-label classification of member participation in online innovation communities

Debaere, Steven; Coussement, Kristof; Ruyck, Tom De

doi:10.1016/j.ejor.2018.03.039

Cited by 20 publications

(16 citation statements)

References 42 publications

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“…The latter two algorithms are problem transformation techniques and used in combination with several binary classifiers. Several authors (Debaere et al, 2018;Read et al, 2009) demonstrate the dominance of the classifier chains method over the binary relevance method. Moreover, Probst et al (2017) proves that the classifier chains method consistently outperforms multi-label random forest and multi-label random ferns on multiple data sets.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating multi-label classifiers and recommender systems in the financial service sector

Bogaert

Lootens

Poel

et al. 2019

European Journal of Operational Research

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The objective of this paper is to evaluate multi-label classification techniques and recommender systems for cross-sell purposes in the financial services sector. We carried out three analyses using data obtained from an international financial services provider. First, we tested four multi-label classification techniques, of which the two problem transformation methods were combined with several base classifiers. Second, we benchmarked the performance of five state-of-the-art recommender approaches. Third, we compared the best performing multi-label classification and recommender approaches with each other. The results identify user-based collaborative filtering as the top performing recommender system, with a cross-validated F1 measure of 42.20% and G-mean of 42.64%. Classifier chains binary relevance with adaboost and binary relevance with random forest are the top performing multilabel classification algorithms for respectively F1 measure and G-mean, yielding a crossvalidated F1 measure of 53.33% and G-mean of 54.37%. The statistical comparison between the best performing approaches confirms the superiority of multi-label classification techniques. Our study provides important recommendations for financial services providers, who are interested in the most effective methods to determine cross-sell opportunities. In previous studies, multi-label classification techniques and recommender systems were always investigated independently of each other. To the best of our knowledge, our study is therefore the first to compare both techniques in the financial services sector.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating multi-label classifiers and recommender systems in the financial service sector

Bogaert

Lootens

Poel

et al. 2019

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…In treating every member equally, theoretically, these approaches could trigger negative reactions. More targeted proactive approaches instead identify members who appear most likely to demonstrate future IMP, using prediction models [2,3]. Here again, the risk is a lower IMP rate if the treatment action triggers negative reactions or involves members who would have participated already on their own accord [37].…”

Section: Contact Strategiesmentioning

confidence: 99%

“…Targeted approaches assume a heterogeneous member base, and recently moderators might rely on analytical models to make objective, cost-effective targeting decisions [3]. With past member profile data and classification techniques, they can construct propensity models to predict future IMP behavior as done in [2] (P(Y = 1|X)). Then moderators can focus their treatment efforts and target only those members with the highest IMP risk.…”

Section: Proactive Targeted Emailmentioning

confidence: 99%

“…In dynamic, innovation-intensive industries, innovation communities (ICs) can grant organizations access to novel knowledge that they can leverage to develop competitive advantages [1,2]. In these private digital networks, companies interact with users to obtain external knowledge [3], as exemplified by Heineken's Idea Brewery, My Starbucks Idea, or Dell's Idea Storm [4].…”

Section: Introductionmentioning

confidence: 99%

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Reducing inferior member community participation using uplift modeling: Evidence from a field experiment

Debaere

Devriendt

Brunneder

et al. 2019

Decision Support Systems

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In their ongoing search for competitive advantage, firms increasingly leverage online innovation communities (ICs). The viability of these ICs may be jeopardized by big data environments and inferior member participation. Therefore, community managers must address poor member participation, together with the data-rich environment. This study examines the viability of a proactive motivational email campaign to reduce inferior member participation using uplift modeling; it also explores optimal treatment characteristics, including message scope (untargeted versus targeted), message content (hedonic, cognitive, and social message), and member profile (self-interest-oriented and positive emotional writing style). The findings indicate that marketing decision makers should use proactive, targeted emails with cognitive motivational elements to mitigate inferior levels of member participation. These findings have important implications for innovation scholars and community managers.

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“…Although GP algorithms have been used to evolve probabilistic trees that search for the optimal topology in bioinformatics (Won et al, 2007) and stock trading (Chen et al, 2009;Ghaddar et al, 2016), to the best of our knowledge, this is the first work that a MOGP algorithm has been used as a multi-class classifier to construct a classification-HMM hybrid model for solving sequential learning problems. Our model can be of interest and easily adapted to other relevant domains in business analytics, such as consumer choice modelling (Sandkci et al, 2008;Blanchet et al, 2016) and high dimensional business data classification or dimension reduction (Debaere et al, 2018;Ghaddar & Naoum-Sawaya, 2018).…”

Section: Introductionmentioning

confidence: 99%

A hybrid model for predicting human physical activity status from lifelogging data

Chen

Allinson

et al. 2020

European Journal of Operational Research

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One trend in the recent healthcare transformations is people are encouraged to monitor and manage their health based on their daily diets and physical activity habits. However, much attention of the use of operational research and analytical models in healthcare has been paid to the systematic level such as country or regional policy making or organisational issues. This paper proposes a model concerned with healthcare analytics at the individual level, which can predict human physical activity status from sequential lifelogging data collected from wearable sensors. The model has a two-stage hybrid structure (in short, MOGP-HMM) -a multi-objective genetic programming (MOGP) algorithm in the first stage to reduce the dimensions of lifelogging data and a hidden Markov model (HMM) in the second stage for activity status prediction over time. It can be used as a decision support tool to provide real-time monitoring, statistical analysis and personalized advice to individuals, encouraging positive attitudes towards healthy lifestyles. We validate the model with the real data collected from a group of participants in the UK, and compare it with other popular two-stage hybrid models. Our experimental results show that the MOGP-HMM can achieve comparable performance. To the best of our knowledge, this is the very first study that uses the MOGP in the hybrid two-stage structure for individuals' activity status prediction. It fits seamlessly with the current trend in the UK healthcare transformation of patient empowerment as well as contributing to a strategic development for more efficient and cost-effective provision of healthcare.

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Multi-label classification of member participation in online innovation communities

Cited by 20 publications

References 42 publications

Evaluating multi-label classifiers and recommender systems in the financial service sector

Evaluating multi-label classifiers and recommender systems in the financial service sector

Reducing inferior member community participation using uplift modeling: Evidence from a field experiment

A hybrid model for predicting human physical activity status from lifelogging data

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