Decision making towards large-scale alternatives from multiple online platforms by a multivariate time-series-based method

With the increasing popularity of Internet-related techniques, decision-making problems with large-scale alternatives from multiple online platforms, such as consumer choice decisions and movie selections, have been emerging hot topics. How to make a selection from a set of alternatives in multiple online platforms is challenge for consumers. In this study, we introduce a multivariate time-series-based decision-making method to solve the problem with large-scale alternatives. Firstly, we set up a multivariate time series from multiple-platforms regarding each alternative. The weights of these platforms are determined based on the information entropy of time series and the number of received evaluations given by platform users. To calculate the information entropy regarding a large number of alternatives, we adopt a time series clustering method to classify alternatives into different clusters, and then calculate the information entropy of clusters and take it as the information entropy of all alternatives. Afterwards, the scores of alternatives are calculated based on the weighted averaging aggregation operator and the alternatives are ranked according to their scores. We demonstrate the effectiveness of the proposed method in guaranteeing the consistency between ranking results and users' consumption behaviors based on real ratings collected from three film-review websites. It is hoped that the proposed method would be helpful for users to intelligently make a selection from large-scale candidate products or services in multiple platforms.