融合动态社交关系的距离度量推荐算法  

Distance metric recommendation algorithm for dynamic social relationships

作　　者：葛君伟[1] 岁飒 方义秋[2] Ge Junwei;Sui Sa;Fang Yiqiu(School of Software Engineering,Chongqing University of Posts&Telecommunications,Chongqing 400065,China;School of Computer Science&Technology,Chongqing University of Posts&Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学软件工程学院,重庆400065 [2]重庆邮电大学计算机科学与技术学院,重庆400065

出　　处：《计算机应用研究》2020年第12期3603-3606,共4页Application Research of Computers

基　　金：重庆市基础与前沿研究计划资助项目(cstc2015jcyjA30001)。

摘　　要：目前,用户的好友关系及其自身呈现的动态变化趋势,使得基于静态社交关系的推荐算法难以满足现今瞬息万变的世界。为解决准确度较低等问题,提出利用用户购买物品的时序行为挖掘隐式社交关系的方法。首先将隐式社交与相似度算法相融合,其次针对近邻评分的稀疏性,提出改进的近邻评分填补方法,然后使用填补后的近邻评分对模型预测评分进行修正,最后生成预测评分。实验部分采用MovieLens数据集评估提出的方法,并与现存算法作对比分析。结果表明,该算法与传统算法及改进算法相比更稳定,也更有效地预测了目标用户的真实评分。Nowadays,user’s friendship relationship and itself are dynamic,therefore,the recommendation algorithm based on static social relationship is difficult to meet the needs of today’s rapidly changing world.To solve the problem of low accuracy,this paper proposed a method utilized user’s time-series behavior of purchasing item to mine implicit social relationship.Firstly,it integrated implicit social and similarity algorithm.Then,it proposed the improved neighbor score filling method for the sparsity of the neighbor score.Finally,it used the filled neighbor score to modify the model prediction score and generate the final prediction score.The experimental part used MovieLens dataset to evaluate the performance of proposed method and compare it with the existing algorithms.The experimental results show that the proposed algorithm is more stable and effective in predicting the true score of target users than traditional algorithms and improved algorithms.

关 键 词：时序影响 近邻集合 社交关系 距离度量 时间衰退 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]