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A Method of Interest Degree Mining Based on Behavior Data Analysis

机译：一种基于行为数据分析的感兴趣挖掘方法

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摘要

Based on big data, this paper starts from the behavior data of users on social media, and studies and explores the core issues of user modeling under personalized services. Focusing on the goal of user interest modeling, this paper proposes corresponding improvement measures for the existing interest model, which has great difference in interest description among different users and it is difficult to find the user interest change in time. For the above problems, this paper takes user-generated content and user behavior information as the analysis object, and uses natural language processing, knowledge warehouse, data fusion and other methods and techniques to numerically analyze user interest mining based on text mining and multi-source data fusion. We propose a user interest label space mapping method to avoid data sparse problem caused by too many dimensions in interest analysis. At the same time, we propose a method to extract and blend the long-term and short-term interests, and realize the comprehensive evaluation of interests. In the analysis of the big data phase, the user preference social property application preference value law, it is expected to achieve user Internet social media application preference data mining from the perspective of big data.
机译：基于大数据，本文从社交媒体用户的行为数据开始，研究并探讨了个性化服务下用户建模的核心问题。专注于用户兴趣建模的目标，本文提出了现有利息模型的相应改进措施，这对不同用户的兴趣描述有很大差异，并且很难找到用户的兴趣随时间变化。对于上述问题，本文将用户生成的内容和用户行为信息作为分析对象，并使用自然语言处理，知识仓库，数据融合和其他方法和技术，以基于文本挖掘和多重分析用户兴趣挖掘源数据融合。我们提出了一个用户兴趣标签空间映射方法，以避免由于兴趣分析中的太多尺寸引起的数据稀疏问题。与此同时，我们提出了一种提取和混合长期和短期利益的方法，实现了对利益的综合评价。在分析大数据阶段，用户偏好社会财产应用偏好值法，预计从大数据的角度来实现用户互联网社交媒体应用偏好数据挖掘。

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