基于主题模型的英语写作批阅系统个性化推荐模块设计与实现  

The Design and Implementation of Personal Recommendation Module in CMET Based on Topic Model

作　　者：葛昊[1] 叶艳[1] 包西林[1] 吴敏[1] 

机构地区：[1]中国科学技术大学现代教育技术中心,合肥230026

出　　处：《科技和产业》2013年第6期151-155,共5页Science Technology and Industry

摘　　要：伴随着自然语言处理技术的发展,计算机代替人工评阅作文已成为可能,计算机评阅的便利,带来了巨大的用户量及作文量,而针对不同用户准确的推荐各自感兴趣的范文可以更加丰富及开阔自己的写作能力。本文提出了一种新的推荐算法,将概率主题模型引入到个性化推荐中,并在实验室的写作批阅平台(简称CMET)上进行了实现。全文介绍了协同过滤推荐和主题模型的相关技术,对传统的协同过滤算法进行了改进,在此基础上,提出了新的推荐算法——基于主题模型的协同过滤(BaseLDACF),并将该推荐算法进行了实现。With the development of Natural Language Processing,it’s achievable to review the composition by computer instead of human.The convenience of the technique also bring about large number of users and compositions,it’s useful for improving the users’writing ability by recommending compositions which the user is interested in accurately.In this article,a new recommendation algorithm which uses probabilistic topic model in personal recommendation is put forward and implemented in the CMET.The article introduces the Collaborative Filtering Recommendation,the Correlated Topic Model and the improvement on the traditional Collaborative Filtering Recommendation Algorithm.Based on the improved Algorithm,the article puts forward a new recommendation algorithm：Collaborative Filtering based on LDA and implements the algorithm.

关 键 词：主题模型 计算机应用 个性化推荐 协同过滤 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]