基于极端评分行为的相似度计算

doi:10.11896/jsjkx.190500130

计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 31-36.doi: 10.11896/jsjkx.190500130

• 数据库&大数据&数据科学 • 上一篇下一篇

基于极端评分行为的相似度计算

冯晨娇^1,2,梁吉业¹,宋鹏³,王智强¹

(山西大学计算智能与中文信息处理教育部重点实验室太原030006)¹;
(山西财经大学应用数学学院太原030006)²;
(山西大学经济与管理学院太原030006)³

收稿日期:2019-05-23 出版日期:2020-02-15 发布日期:2020-03-18
通讯作者: 梁吉业(ljy@sxu.edu.cn)
基金资助:
国家自然科学基金项目(61876103);山西省重点研发计划重点项目(201603D111014);山西省回国留学人员科研资助项目(2017-005);山西省1331工程项目

New Similarity Measure Based on Extremely Rating Behavior

FENG Chen-jiao^1,2,LIANG Ji-ye¹,SONG Peng³,WANG Zhi-qiang¹

(Key Laboratory of Computation Intelligence & Chinese Information Processing (Shanxi University),Ministry of Education,Taiyuan 030006,China)¹;
(College of Applied Mathematics,Shanxi University of Finance and Economics,Taiyuan 030006,China)²;
(School of Economics and Management,Shanxi University,Taiyuan 030006,China)³

Received:2019-05-23 Online:2020-02-15 Published:2020-03-18
About author:FENG Chen-jiao,born in 1977,doctorial student,lecturer,is member of China Computer Federation.Her main research interests include data mining,big data correlation analysis and recommender systems;LIANG Ji-ye,born in 1962,Ph.D,professor,Ph.D supervisor,is member of China Computer Federation.His main research interests include granular computing,data mining and machine learning.
Supported by:
This work was supported by the National Natural Science Foundation of China (61876103), Projects of Key Research and Development Plan of Shanxi Province (201603D111014), Research Project Supported by Shanxi Scholarship Council of China (2017-005) and 1331 Engineering Project of Shanxi Province, China.

摘要/Abstract

摘要： 随着互联网技术的迅猛发展,互联网信息急剧增长,信息过载问题愈发凸显。面对海量的互联网信息,用户往往需要耗费大量的时间来搜索所需的信息或产品,而搜索的解往往受到制约。为解决信息过载问题,推荐系统应运而生。推荐系统根据用户的历史行为推测其需求、兴趣等,将用户感兴趣的信息、产品等推荐给用户。作为推荐领域中一类重要的推荐方法,基于记忆的协同过滤方法通常依据用户或产品的近邻信息来构造评分预测函数,其核心在于准确度量用户或产品之间的相似度。传统的相似度量,如皮尔逊、余弦及秩相关系数等,通常只考虑了用户之间的线性关系;而启发式相似度如基于3个特殊因子的PIP相似度及其改进方法,则只刻画了用户之间的非线性关系。事实上,在推荐系统中,就用户之间的相似关系而言,仅用线性或是非线性函数来度量均是不准确的。为了更为精细地刻画用户之间的相似程度,文中提出了基于非线性函数的用户极端评分行为的相似程度度量指数,通过将该指数融入传统的线性相关系数,构造了一个考虑极端评分行为的新的相似度。为验证该方法的有效性,基于Ml(100k)和Ml-latest-small两个数据集,将其与传统相似度以及启发式相似度进行比较,结果显示基于极端评分行为相似度的协同过滤方法在MAE和RMSE指标上能够获得更好的表现。

关键词: 基于记忆的协同过滤, 极端评分行为, 推荐系统, 相似度, 协同过滤

Abstract: With the rapid development of Internet technology,drastic Internet information explosion makes information overload as an increasingly serious problem.Faced with the massive Internet information,users consume a lot of time to search for information or products,but the search solution is constrained.The recommender systems is hence proposed to address the problem of information overload.The recommender systems use users’ historical behaviors to speculate their needs,interests,etc.,and recommend the information and products users may be interested in.As an important type of recommendation approach,the memory-based collaborative filtering methods establish the rating prediction function based on neighbor information of the user or pro-duct.The essence of the function is to precisely measure the similarity between users or products.The traditional similarity mea-sures such as Pearson,Cosin and Spearman rank correlation coefficients,only take into account the linear relationship between users,while the heuristic similarities,such as the PIP measurement based on three special factors and its improved version,only depict the non-liner relationship between users.Indeed,in the recommender systems,it is neither the linear relation nor the non-linear relation is good for measuring the similarity between users.In order to describe the similarity among users more finely,this paper proposed a similarity measure index of the correlation level considering the extreme rating behaviors based on anonli-near function.By integrating this index with the traditional linear correlation coefficients,this paper constructed a novel similarity measure.Comparative experiments were conducted to test the practicability and validity of the proposed approach on Ml (100k) and Ml-latest-small datasets.The results demonstrate that the proposed method performs better judged by indicators of MAE and RMSE.

Key words: Collaborative filtering, Extremely rating behavior, Memory-based collaborative filtering, Recommender systems, Similarity

中图分类号:

TP182

冯晨娇,梁吉业,宋鹏,王智强. 基于极端评分行为的相似度计算[J]. 计算机科学, 2020, 47(2): 31-36. https://doi.org/10.11896/jsjkx.190500130

FENG Chen-jiao,LIANG Ji-ye,SONG Peng,WANG Zhi-qiang. New Similarity Measure Based on Extremely Rating Behavior[J]. Computer Science, 2020, 47(2): 31-36. https://doi.org/10.11896/jsjkx.190500130

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基于极端评分行为的相似度计算

New Similarity Measure Based on Extremely Rating Behavior

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