Time-aware customer preference sensing and satisfaction prediction in a dynamic service market

In the dynamic service market, massive services and variations of their Quality of Services (QoS) and service contract make it difficult for customers to acquire the information of all the services comprehensively and timely. As a result, customers cannot raise accurte expectations. A customer has to choose services in terms of the incomplete information of the dynamic service market to achieve higher Satisfaction Degree (SD) as much as possible. Besides, because a customer’s preferences vary over time, his SD is also time-aware. Therefore, for service providers, to accurately recommend services to customers, it is necessary to sense the customer preferences varying against time and predict personalized customers’ satisfaction. To address this challenge, we propose a time-aware customer preference sensing and satisfaction prediction method based on customer’s service usage history and change history of services. Firstly, the customer satisfaction model on contract-based services is proposed to measure customers’ satisfaction for services. Then, we adopt the box-plot method and the frequency histogram to sense time-aware customer preferences. In addition, a time-aware personalized SD prediction algorithm called SDPred is presented to predict the missing values due to information asymmetry. Meanwhile, several experiments have been conducted based on a released data set, which verify the effectiveness of our methods. Besides, the impact of parameter settings in the SDPred algorithm is further studied, which provides more evidences to illustrate the superiority of our method.