ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (5): 977-985.doi: 10.7544/issn1000-1239.2018.20160924

• 人工智能 • 上一篇    下一篇

基于基准相似空间分布优化的偏好预测方法

高岭1,2,高全力1,王海2,王伟2,杨康2   

  1. 1(西安工程大学计算机科学学院 西安 710048); 2(西北大学信息科学与技术学院 西安 710127) (gl@nwu.edu.cn)
  • 出版日期: 2018-05-01
  • 基金资助: 
    国家自然科学基金项目(61373146,61572401,61672426); 陕西省教育厅科学研究项目(2013JK1178)

A Preference Prediction Method Based on the Optimization of Basic Similarity Space Distribution

Gao Ling1,2, Gao Quanli1, Wang Hai2, Wang Wei2,Yang Kang2   

  1. 1(College of Computer Science, Xi’an Polytechnic University, Xi’an 710048); 2(School of Information Science and Technology, Northwest University, Xi’an 710127)
  • Online: 2018-05-01

摘要: 针对现有推荐系统所采用的行为相似度度量方法,受数据稀疏性的影响难以获取到用户真正的偏好最近邻,影响了推荐准确度的问题,提出了一种结合基准相似空间分布优化的用户偏好获取方法.首先通过余弦相似度、修正的余弦相似性、皮尔森相关系数等偏好行为相似程度度量方法,获取用户与用户间原始的偏好行为近似程度,根据近似程度的分布特征首先获取偏好中心点,并根据偏好行为近似程度距偏好中心点的行为距离获取平均相似幅度,进而生成基准相似空间,通过建立基于平均近邻与异常评分交互影响的修正模型,优化基准相似空间,并据此为用户生成推荐列表.在大规模真实数据集上的实验结果表明:所提出方法与现有方法WSCF与OTCF相比,平均绝对误差分别降低了12.8%与9.7%,覆盖率分别提升了5.79%与3.83%,多样性与WSCF基本一致,相比OTCF增加了近4.3%,即是所提出方法提升了推荐精度与推荐质量.

关键词: 偏好中心点, 平均近邻, 相似修正, 基准相似空间, 推荐系统

Abstract: The similarity measure methods of preference behavior in the existing collaborative filtering based recommender systems are unable to acquire the real nearest neighbors, which have influenced the prediction accuracy. To solve this problem, an users’ preference prediction method based on the optimization of basic similarity space distribution is proposed. In the beginning, this method uses cosine similarity, constrained cosine similarity and Pearson correlation coefficient to get the original similarities among users. Secondly, it generates the preference center based on the distribution characteristic of users’ preference similarity, and then it get the average similarity range based on the behavior distance between other preference behavior and preference center to build the basic similarity space. Finally, the method generates the modified model based on average nearest neighbors and abnormal ratings to optimize the basic similarity space, and basing on which generate predictions for users. The authors present empirical experiments by using a real extensive data set. Experimental results show that the proposed method can achieve lower MAE about 12.8% and 9.7% compared with WSCF and OTCF, and the coverage rate is increased about 5.79% and 3.83%, and the diversity is the same with WSCF and is increased about 4.3% compared with OTCF, which indicates that the proposed method can efficiently improve recommendation quality.

Key words: preference center, average nearest neighbor, similarity modify, basic similarity space, recommender system

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