摘要
现代电子商务系统用户和物品数目的日益增加使得User-Item矩阵变得越来越稀疏,再加上目前相似性度量方法均存在一定弊端,致使推荐系统的推荐质量降低了。针对传统混合模型推荐算法做了优化,其相似性度量方法由物品属性相似性和改进的修正余弦相似性线性组合而成,权重因子自动生成,考虑了用户评分尺度及用户活跃度对物品相似性的影响。为解决冷启动问题,使用用户基本信息获得用户间的相似度,各属性权重因子由SVDFeature计算得到。实验结果表明,该算法有效地提升了推荐系统的推荐质量,同时还有效解决了用户冷启动与物品冷启动问题。
The ever-increasing number of users and items of modem electronic commercial system has made the user-item matrix to become more and more sparse.This situation,in combination with somewhat inappropriate similarity cal-culation methods currently used,maks the recommendation quality of recommender system to gradually reduce.For this,we presented an optimized recommender algorithm which is based on a hybrid model.In our algorithm,the similari-ty function is a linear combination of the item property similarity and a modified correlation cosine similarity.The weighting factor,which is generated automatically,is related to the number of users who rated both items.The modifi-cation to the correlation cosine similarity measure considers both the rating tendency and the activity from users.To deal with the cold start problem,we also acquired user similarity through user property information with weighting factors computed by SVDFeature.The experimental results demonstrate that our algorithm effectively improves the recom-mendation quality and alleviates cold starting problem resulting from both users and items.
作者
李鹏飞
吴为民
LI Peng-fei;WU Wei-min(Department of Computer Engineering,School of Computer,Beijing Jiaotong University,Beijing 100044,China)
出处
《计算机科学》
CSCD
北大核心
2014年第2期68-71,98,共5页
Computer Science
关键词
协同过滤
相似度
混合模型
权重因子
冷启动
Collaborative filtering
Similarity
Hybrid recommendation
Weighting factor
Cold start
