Computational Experiment-Based Evaluation on Context-Aware O2O Service Recommendation

Abstract

O2O (online to offline) service recommendation is a typical context-aware service application, which needs to provide the most suitable services to customers in time according to their user profile and current context. By means of composing various data sources, different O2O service recommendation strategies can be customized, which may lead to great performance difference. Incorrect or non-real-time service recommendation would not work well and even cause negatives consequences. As a result, how to evaluate the performance of different O2O service recommendation strategies and select the most suitable one has become a key problem in the field. Due to the diversity and the variability of context events, as well as the economic, legal, and ethical impact, it is difficult or even impossible for traditional methods to realize comprehensive evaluation of various service strategies. Based on the background, this paper proposes a computational experiment-based evaluation method of O2O service recommendation strategies, which mainly consists of three parts: customization of O2O service strategies, modeling of experiment system, and execution of experiment evaluation. As a case study, the method was applied to Food O2O service. Three kinds of service strategies were compared respectively under two different market environments. Experiment results show that the proposed evaluation method is effective.