一种改进的基于兴趣相似度推荐算法  

An Improved Recommendation Algorithm Based on Interest Similarity

作　　者：柯翔敏[1] 陈江[1] 罗光华[1] KE Xiangmin;CHEN Jiang;LUO Guanghua(Center of Network and Educational Technology,Huaqiao University,Xiamen,Fujian 361021,China)

机构地区：[1]华侨大学网络与教育技术中心,福建厦门361021

出　　处：《计算机工程》2020年第8期78-84,共7页Computer Engineering

基　　金：福建省中青年教师科研项目(JZ180187,JZ180193)。

摘　　要：协同过滤推荐算法通过对用户行为进行相似度计算来实现目标推荐,但传统协同过滤算法的相似度计算存在一定的失真性。针对该问题,依据越不流行的物品兴趣分配权重越大的思想,提出逆流行度与共同兴趣项的概念,并设计一种相似度计算方法。在相似度计算时降低流行度高的物品的权重,从而减小热门物品对用户个性化的影响,同时提高共同兴趣数量对相似度影响的权重。在此基础上,建立一种新的推荐模型从而为目标用户推荐相似度最高的用户集。在数据集MovieLens上的实验结果表明,该相似度计算方法能够取得较好的推荐效果,其精确率、召回率及F1值优于Cosin、Pearson和Corrcosin方法。Collaborative filtering recommendation algorithms recommend commodities by calculating the similarity of user behavior,but the similarity calculation of traditional collaborative filtering algorithms has a certain degree of distortion.In view of the problem,this paper proposes the concept of inverse popularity and common interest item based on the idea that the less popular items are assigned more weight.On this basis,a similarity calculation method is proposed,which reduces the weight of items with high popularity in the calculation of similarity,so as to reduce the influence of popular items on user personalization.Also,it increases the weight of the influence of the common interest number on similarity.On this basis,a new recommendation model is established to find the user set with the highest similarity to the target user.Experimental results on the MovieLens dataset show that the proposed similarity calculation method can improve the recommendation performance,and achieve a higher precision rate,recall rate and F1 score than Cosin,Pearson and Corrcosin methods.

关 键 词：推荐算法 协同过滤 相似度 兴趣分配 逆流行度 共同兴趣项 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]