| 基于情境相似度和二次聚类的协同过滤推荐算法 |
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| 引用本文： | 蔡海尼,覃梦秋,文俊浩,熊庆宇,黎懋靓.基于情境相似度和二次聚类的协同过滤推荐算法[J].计算机科学,2016,43(4):210-213, 234. |
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| 作者姓名： | 蔡海尼 覃梦秋 文俊浩 熊庆宇 黎懋靓 |
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| 作者单位： | 重庆大学软件学院 重庆400044,重庆大学软件学院 重庆400044,重庆大学软件学院 重庆400044,重庆大学软件学院 重庆400044,重庆大学软件学院 重庆400044 |
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| 基金项目： | 本文受国家自然科学基金(61379158,7),中央高校基本科研业务费科研专项(CDJXS11181161)资助 |
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| 摘 要： | 随着移动互联网规模的不断扩大,传统推荐系统因较少考虑多种情境因素和用户置信度对用户偏好预测的综合影响,造成了推荐算法预测结果的偏差。针对此问题,将情境信息引入个性化推荐的过程中,提出一种基于情境相似度和二次聚类的协同过滤算法。该算法首先根据用户情境的相似度对用户进行初始聚类,再基于评分矩阵计算用户评分置信度,将用户分为核心用户和非核心用户；然后根据核心用户评分对初始聚类的簇心进行调整,并对簇中非核心用户进行重聚类,形成新的聚簇；最终根据情境相似度对用户偏好进行预测。该算法可以在一定程度上降低评分矩阵中的噪点对聚类结果的影响,提高了推荐结果的准确性。基于实际数据集的仿真实验表明,该算法与传统协同过滤算法相比能够有效提高用户偏好预测的准确性,增加协同过滤推荐算法的精确度。
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| 关 键 词： | 推荐系统 情境相似度 协同过滤 核心用户 二次聚类 |
| 收稿时间： | 2015/3/29 0:00:00 |
| 修稿时间： | 7/7/2015 12:00:00 AM |
Collaborative Filtering Recommendation Algorithm Based on Context Similarity and Twice Clustering |
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| CAI Hai-ni,QIN Meng-qiu,WEN Jun-hao,XIONG Qing-yu and LI Mao-liang.Collaborative Filtering Recommendation Algorithm Based on Context Similarity and Twice Clustering[J].Computer Science,2016,43(4):210-213, 234. |
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| Authors: | CAI Hai-ni QIN Meng-qiu WEN Jun-hao XIONG Qing-yu and LI Mao-liang |
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| Affiliation: | School of Software Engineering,Chongqing University,Chongqing 400044,China,School of Software Engineering,Chongqing University,Chongqing 400044,China,School of Software Engineering,Chongqing University,Chongqing 400044,China,School of Software Engineering,Chongqing University,Chongqing 400044,China and School of Software Engineering,Chongqing University,Chongqing 400044,China |
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| Abstract: | Aiming at solving the problem of personalized service recommendation in the field of mobile telecommunication network,this paper introduced the context information to the process of personalized recommendation,and proposed a collaborative filtering algorithm based on context similarity and twice clustering.Firstly,according to user context similarity,the users are clustered,and user rating confidence is calculated based on the rating matrix to distinguish core users from non-core users.Secondly,the center of clusters formed by initial clustering can be adjusted according to the rating of core users,and non-core users will be clustered again to form a new cluster.Finally,according to the context similarity,user preferences will be predicted.To some extent,this algorithm can reduce the influence of noise data from rating matrix on clustering results,and reduce the deviation of the recommendation.The experiment based on the simulation data set shows that,the algorithm improves the accuracy of user preferences effectively,and increases the accuracy of collaborative filtering recommendation. |
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| Keywords: | Recommendation system Context similarity Collaborative filtering Core users Twice clustering |
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