一种基于时效近邻可信选取策略的协同过滤推荐方法  

Time-effective Nearest Neighbor Trusted Selection Strategy Based Collaborative Filtering Recommendation Method

作　　者：韩志耕[1,2] 范远哲 陈耿 周婷 HAN Zhigeng;FAN Yuanzhe;CHEN Geng;ZHOU Ting(School of Computer Science/School of Intelligence Audit,Nanjing Audit University,Nanjing 211815,China;Jiangsu Key Laboratory of Audit Information Engineering,Nanjing 211815,China;School of Accounting,Nanjing Audit University,Nanjing 211815,China)

机构地区：[1]南京审计大学计算机学院/智能审计学院,南京211815 [2]江苏省审计信息工程重点实验室,南京211815 [3]南京审计大学会计学院,南京211815

出　　处：《计算机科学》2023年第S02期506-516,共11页Computer Science

基　　金：国家自然科学基金项目(72072091);江苏省高校自然科学基金项目(21KJA520002,22KJA520005);江苏省研究生科研与实践创新计划项目(KYCX23_2345);审计信息工程与技术协同创新中心项目。

摘　　要：传统协同过滤推荐通常基于数据是静态的假设,在数据稀疏时存在推荐精度低下的问题。为解决该问题,一些研究尝试向推荐策略中添加有关用户兴趣变化、推荐能力可信度等补充信息,但对误导或干扰推荐的恶意用户兴趣策略变化和推荐能力波动等异常情况欠缺考虑,系统抗攻击性、推荐稳定性与可信性均难以得到保证。通过引入兴趣时效相似度和推荐信度重估两个概念,提出了一种基于时效近邻可信选取策略的协同过滤推荐方法。该方法充分考虑了影响目标用户近邻筛选质量的用户兴趣异常变化和推荐能力波动两个关键因素,构建了包含时效近邻筛选、可信近邻选取和评分预测3个策略的推荐流程。在MovieLens数据集和亚马逊video game数据集上,利用平均绝对误差,平均预测增量,攻击用户查准率、查全率和调和平均等评估指标,对所提策略与其他6种基准策略进行了比较。结果显示,新策略在推荐精度、抗攻击力和攻击者识别力上均有明显的性能提升。The traditional collaborative filtering(CF)recommendation is usually based on the assumption that the data is static.When the data is sparse,it usually leads to low recommendation accuracy.With this in mind,some studies try to add supplemen-tary information such as changes in user interest and the trustiness of recommendation ability to their strategies.However,most of them lack of consideration for the abnormal situations that mislead or interfere with the recommendation,such as malicious changes in user interests and fluctuations in the recommendation ability,and are difficult to ensure the anti-attack,recommendation stability and reliability of the recommendation system.By introducing interest time-effective similarity and re-evaluation on re-commendation trust degree,this paper proposes a time-effective nearest neighbor trusted selection strategy based collaborative filtering recommendation method.It takes into account two key factors,that is,the abnormal change of user interest and the fluctuation of user recommendation ability,which affect the quality of target user’s neighbor filtering,and construct a recommendation process that includes three strategies,that is,time-effective nearest neighbor selection,trusted nearest neighbor selection and ra-ting prediction.Based on MovieLens dataset and Amazon video game dataset,and with the metrics such as mean absolute error(MAE),average prediction shift(APS),and attacker identification of precision ratio,recall ratio and F1 means,the performance of the proposed strategy and other six baselines are compared.The results show that our strategy outperform the baselines in re-commendation accuracy,anti-attack and attacker identification.

关 键 词：协同过滤 时效近邻 可信近邻 推荐精度 抗攻击 攻击者识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]