基于物品关联协同过滤的下一购物篮推荐算法  

Next-basket Recommendation Algorithm Based on Correlation Between Items Collaborative Filtering

作　　者：蒋滨泽 邓欣[1] 杜雨露 张恒 JIANG Binze;DENG Xin;DU Yulu;ZHANG Heng(College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学计算机科学与技术学院,重庆400065

出　　处：《计算机科学》2023年第S02期474-479,共6页Computer Science

基　　金：重庆市自然科学基金(cstc2020jcyj-msxmX0284);重庆市教委科学技术研究项目(KJQN202000625)。

摘　　要：下一购物篮推荐系统的目标是根据用户的历史购物篮序列,为用户推荐下一个购物篮可能购买的物品。然而现有的方法侧重于把购物篮内的每个物品看作是独立的部分进行推荐,忽略了购物篮内物品之间的联系,从而影响推荐结果的准确性。针对这一问题,文中提出了一种基于物品关联协同过滤的下一购物篮推荐算法(Correlation Between Items Collaborative Filtering,CBICF)。首先对用户的历史购物篮序列进行建模生成用户的个性化物品频率信息,并用它对用户进行近邻聚类;然后通过物品关联性度量方法生成物品关联矩阵,以加权融合的方式来获取用户偏好物品的关联物品信息,从而提高推荐的准确度。在两个真实数据集上进行实验比较与分析,结果表明该算法在各评价指标上均优于对比算法。特别是在探索新物品的情形中,所提方法的推荐准确度相比于其它基于协同过滤的方法有显著提升。The next-basket recommendation system aims to recommend items that could be seen in their next-basket,based on the sequence of users’historical baskets.However,the existing methods focus on the recommendation of each item in the shopping basket as an independent part,ignoring the relationship between items in the shopping basket,which impacts on recommendation accuracy.To solve this problem,a next-basket recommendation algorithm based on correlation between items collaborative filtering(CBICF)is proposed.Firstly,the historical shopping basket sequence of users is modeled to generate users’personalized item frequency information,which is used for user’s nearest neighbor clustering.Then,item correlation matrix is generated by correlation between items measurement method,and the associated item information of the users’preference items is obtained by weighted fusion method,to improve the accuracy of recommendations.Experimental comparison and analysis on two real data sets reveal that the proposed algorithm is superior to the comparison algorithm in indicators.Especially in the case of exploring new items,the accuracy of recommending is significantly improved compared with other methods based on collaborative filtering.

关 键 词：下一购物篮推荐 协同过滤 聚类 物品关联 个性化物品频率信息 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]