一种基于离散增量的项目相似性度量方法  

Measuring Item Similarity Based on Increment of Diversity

作　　者：王永[1] 王永东 郭慧芳 周玉敏[1] Wang Yong;Wang Yongdong;Guo Huifang;Zhou Yumin(Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065, Chin)

机构地区：[1]重庆邮电大学电子商务与现代物流重点实验室,重庆400065

出　　处：《数据分析与知识发现》2018年第5期70-76,共7页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目"结合知识图谱的概率话题模型研究"(项目编号:61502066);重庆市基础与前沿项目"面向产品评论的细粒度观点挖掘方法研究"(项目编号:cstc2015jcyj A40025);重庆市社会科学规划项目"电子商务产品评论中情感分析模型及应用"(项目编号:2015SKZ09)的研究成果之一

摘　　要：【目的】缓解典型的项目相似性度量方法必须使用共同评分、在高度稀疏数据环境中预测精度较低等问题。【方法】引入生物信息科学领域的离散增量,将其构造为相异系数,利用项目评分值的频数及其分布计算项目相似度,克服依赖于共同评分的局限性,改善数据稀疏性的问题;同时结合项目属性信息,提高度量结果的合理性与准确性。【结果】相比于其他典型算法,本文算法的RMSE降低了2.56%,F1值提高了3.88%。【局限】推荐多样性可能不足。【结论】本文算法对于冷启动问题亦有更好的表现,因此,具有良好的应用潜力。[Objective] This study aims to solve the issues facing traditional methods measuring item similarity, such as using common rating and poor prediction accuracy in highly sparse data environment. [Methods] First, we constructed the dissimilarity coefficient with the increment of diversity from bioinformatics. Then, we calculated item similarity according to the frequency and distribution of ratings, which effectively addressed the data sparsity issue. Finally, we improved the accuracy of measurement with the item attributes. [Results] Compared with traditional algorithms, the proposed method reduced RMSE by 2.56%, and then increased the F value by 3.88%. [Limitations] The diversity of our recommendation might be insufficient. [Conclusions] The proposed method could effectively measure item similarity.

关 键 词：离散增量 相似性度量 数据稀疏性 协同过滤 冷启动 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]