摘要：

现有多数新闻推荐方法将用户兴趣划分为感兴趣和不感兴趣,难以对用户兴趣实现更精细的描述。针对该问题,将情感空间的思想融合到推荐系统中,提出一种以情感为依据的阅读兴趣表述方法。采用广义回归神经网络对眼动追踪数据的分析,提取阅读兴趣和眼动追踪数据之间的关系,建立基于眼动数据的用户兴趣模型。实验结果表明,该模型预测准确率达到86%,比PLSR模型高7%,具有较高的实用性。

关键词: 情感模型, 阅读兴趣模型, 眼动追踪, 情感量化, 广义回归神经网络

Abstract:

The existing news recommendation methods mostly divide user’s interest into interest and not interest,which is difficult to achieve a more detailed description of user’s interest.In order to solve this problem,this paper introduces the idea of emotional space to propose a expressing method of reading interest based on emotion.In details,the Generalized Regression Neural Network (GRNN) is used to analyze eye tracking data and extract the relations between the eye tracking data and reading interest.And then,a user interest model is proposed based on the analyzing results.Experimental results show that the prediction accuracy of the proposed model is 86%,which is 7% higher than that of the PLSR model.It means that the proposed model has a good practicality.

Key words: emotional model, reading interest model, eye tracking, emotional quantification, Generalized Regression Neural Network (GRNN)

中图分类号: 

• TP399