关键词: 动态用户行为模型, 用户偏好, 记忆曲线, 增量更新, MapReduce

Abstract: Analyzing historical user behavior data and establishing user preference model by some certain method is the critical subject with great attention. This paper considers the time series characteristics of data generation and the influence of the variables with temporal characteristics on user behavior models. Based on the forgetting curve in psychology, this paper starts from user behavior data and gives the representation of user preferences. Thus, a forgetting curve model can be established for each preference and user preferences can be represented by real time manner. Aiming at massive user behavior data, this paper proposes the MapReduce-based algorithms for the incremental    update of model parameters and the computation of dynamic user preferences. Thus, inherently dynamic user preferences can be reflected by the constructed user behavior model. The experimental results conducted on real data show that the proposed model and algorithms are efficient, correct and applicable.

Key words: dynamic user behavior model, user preference, forgetting curve, incremental update, MapReduce