计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 221000076-6.doi: 10.11896/jsjkx.221000076
蒋滨泽, 邓欣, 杜雨露, 张恒
JIANG Binze, DENG Xin, DU Yulu, ZHANG Heng
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。
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