实时个性化微博推荐系统

doi:10.11896／j.issn.1002-137X.2018.09.042

摘要/Abstract

摘要： 目前很多社交网络服务对用户的个性化需求考虑得不充分,并且社交网络服务由于需要处理海量数据而难以保障服务的实时性。为了实时响应用户在微博推荐中的个性化请求,提高推荐的效率和质量,提出了一种基于LDA主题模型和KL散度相结合的RPMPS微博推荐模型。RPMPS推荐模型不但通过文档-主题概率分布矩阵获得了用户信息与待推荐微博的主题相似性,而且还通过文档-词来对词频概率进行统计,从而获得用户信息与待推荐微博的内容相似性。最后,基于RPMPS推荐模型构建实时个性化微博推荐系统,并在数据处理过程中对微博进行过滤以缩短系统的响应时间。通过真实数据集验证了系统可较好地满足用户的实时个性化需求。

关键词: RPMPS推荐模型, 社交网络, 推荐系统, 微博

Abstract: At present,many social networking services do not fully consider the personalized needs of users,and it is difficult to guarantee the real-time services because social networking services need to deal with massive amounts of data.A micro-blog recommendation model called RPMPS based on LDA topic model and KL divergence was proposed to respond to users’ personalized request in micro-blog recommendation in real time and improve the efficiency and quality of recommendation.RPMPS model not only uses the document-topic probability distribution matrix to get the similarity between the topic of user information and the topic of candidate micro-blog,but also obtains the similarity between the content of user information and the content of candidate micro-blog by utilizing the document-word to count the probability of the word frequency .At last,the real-time personalized micro-blog recommendation system based on RPMPS model is constructed,and micro-blog is filtered during the course of data processing to shorten the system response time.Experimental results on real-world datasets demonstrate that the system can meet the real-time personalized demands of users.

Key words: Micro-blog, Recommendation system, RPMPS recommendation model, Social network

中图分类号:

TP319

刘慧婷, 程雷, 郭孝雪, 赵鹏. 实时个性化微博推荐系统[J]. 计算机科学, 2018, 45(9): 253-259. https://doi.org/10.11896／j.issn.1002-137X.2018.09.042

LIU Hui-ting, CHENG Lei, GUO Xiao-xue, ZHAO Peng. Real-time Personalized Micro-blog Recommendation System[J]. Computer Science, 2018, 45(9): 253-259. https://doi.org/10.11896／j.issn.1002-137X.2018.09.042

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