An O2O Service Recommendation Algorithm Based on User Context and Trust Service
Hongfang Han, Ang Gao, Jianji Ren, Yongqiang Ma, Xue Xiao · 2016
O2O (Online to Offline) is a new type of e-commerce model. Traditional recommendation methods focus on commodity rather than service, and lack the consideration on customer context and service status. In order to improve the quality of O2O service recommendation, this paper proposes an O2O service recommendation algorithm based on customer context and trust service. The method mainly includes two parts: (1) According to context sensitivity of service recommendation, a collaborative filtering recommendation algorithm is proposed which integrates basic user data and user context data, (2) In order to improve trust degree of service recommendation, the state QoS (Quality of Service) attributes of O2O service is updated continuously to ensure the availability of O2O service. Finally, two experiments are carried out based on the data set, and the experimental results show that the proposed algorithm has significant improvements in accuracy and trust degree of O2O service recommendation.