摘要
针对传统的基于项目的协同过滤推荐算法中数据稀疏问题,以及受时间效应影响推荐准确度较低问题,提出将隐马尔科夫模型与传统的基于项目的协同过滤推荐算法相融合的推荐算法HMM-Item CF。算法通过隐马尔科夫模型对系统中所有用户的评分行为,与目标用户的历史评分行为进行统筹分析,找到一批用户下一时刻概率最高的评分对象,并将这些评分对象发生概率与传统的项目相似度计算方法相加权得到新的相似度,最终产生推荐结果。仿真实验中对算法的重要参数进行训练,并与其他算法进行对比,证明改进后的算法是有效的。
In view of the problems of the traditional collaborative filtering recommendation algorithm based on the project of data sparseness and the low accuracy of recommendation, the thesis puts forward the HMM-ItemCF recommendation algorithm which combines Hidden Markov Model with the traditional collaborative filtering recommendation algorithm based on the project. The al-gorithm using Hidden Markov Model to all the users in the system evaluation behavior and the history of the target user behavior to carry on the overall analysis, to find the probability of the next moment a group of users with the highest score object, and the probability of occurrence of these scores with traditional objects project weighted similarity calculation method to get a new recom-mendation similarity ultimately produce results. The simulation experiment is carried out on the algorithm with an important pa-rameter in the training, and compared with other algorithms. It proves that the improved algorithm is effective.
出处
《计算机与现代化》
2015年第9期60-65,共6页
Computer and Modernization
基金
国家自然科学基金资助项目(61003180/F020509)
江苏省自然科学基金资助项目(BK2010683)
关键词
协同过滤
数据稀疏
时间效应
隐马尔科夫
collaborative filtering
sparse data
time effect
Hidden Markov
