基于用户偏好深度挖掘的体验型产品改进

Deep Mining User Preferences for Experience Product Improvement

就复杂体验型产品的在线评论不一致和价值密度低问题,提出用户偏好深度挖掘模型,支撑企业依据产品评论精准改进现有产品。首先,建立双向长短期记忆神经网络(BiLSTMNN)模型,细粒度挖掘粗略评论中隐含的用户情感极性;其次,为了从不一致的评论中挖掘用户偏好,应用偏回归模型挖掘用户对不同产品属性的线性偏好;最后,根据训练好的偏回归模型,将Kano模型应用于发现用户对各种产品属性的非线性偏好。以上海迪士尼乐园的数据为例,用户偏好深度挖掘模型得到验证,能够以较高的精确度挖掘复杂体验型产品评论中所隐含的用户非线性偏好,并据此提出产品的改进建议。

Aiming at the shortcomings of low-value density and inconsistency in online reviews of complex experiential products,a deep mining model of user preferences is proposed to support enterprises to improve existing products based on product reviews accurately. First,establish a bidirectional long and short-term memory neural network(BiLSTMNN) to fine-grained mining the user e motional polarity implicit in the rough comments. Secondly,in order to mine user preferences from inconsistent comments,a partial regression model is used to mine users’ perceptions of different attributes Linear preference. Finally,according to the trained partial regression model,the Kano model is applied to discover the non-linear preferences of users with various attributes. Taking the data of Shanghai Disneyland company as an example,the user preference deep mining model designed in this paper can mine the user’s non-linear preferences implied in complex experiential product reviews with high accuracy and then put forward suggestions for product improvement.