TY - JOUR
T1 - A framework for configuring collaborative filtering-based recommendations derived from purchase data
AU - Geuens, Stijn
AU - Coussement, Kristof
AU - De Bock, Koen W.
PY - 2018/2/16
Y1 - 2018/2/16
N2 - This study proposes a decision support framework to help e-commerce companies select the best collaborative filtering algorithms (CF) for generating recommendations on the basis of online binary purchase data. To create this framework, an experimental design applies several CF configurations, which are characterized by different data-reduction techniques, CF methods, and similarity measures, to binary purchase data sets with distinct input data characteristics, i.e., sparsity level, purchase distribution, and item–user ratio. The evaluations in terms of accuracy, diversity, computation time, and trade-offs among these metrics reveal that the best-performing algorithm in terms of accuracy remains consistent regardless of the input-data characteristics. However, for diversity and computation time, the best-performing model varies with the input characteristics. This framework allows e-commerce companies to decide on the optimal CF configuration as a function of their specific binary purchase data sets. They also gain insight into the impact of changes in the input data set on the preferred algorithm configuration.
AB - This study proposes a decision support framework to help e-commerce companies select the best collaborative filtering algorithms (CF) for generating recommendations on the basis of online binary purchase data. To create this framework, an experimental design applies several CF configurations, which are characterized by different data-reduction techniques, CF methods, and similarity measures, to binary purchase data sets with distinct input data characteristics, i.e., sparsity level, purchase distribution, and item–user ratio. The evaluations in terms of accuracy, diversity, computation time, and trade-offs among these metrics reveal that the best-performing algorithm in terms of accuracy remains consistent regardless of the input-data characteristics. However, for diversity and computation time, the best-performing model varies with the input characteristics. This framework allows e-commerce companies to decide on the optimal CF configuration as a function of their specific binary purchase data sets. They also gain insight into the impact of changes in the input data set on the preferred algorithm configuration.
KW - Binary purchase data
KW - Collaborative filtering
KW - E-commerce
KW - OR in marketing
KW - Recommendation systems
UR - http://www.scopus.com/inward/record.url?scp=85024500511&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2017.07.005
DO - 10.1016/j.ejor.2017.07.005
M3 - Article
AN - SCOPUS:85024500511
SN - 0377-2217
VL - 265
SP - 208
EP - 218
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 1
ER -