计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 159-164.doi: 10.11896/jsjkx.210300263
陈壮, 邹海涛, 郑尚, 于化龙, 高尚
CHEN Zhuang, ZOU Hai-tao, ZHENG Shang, YU Hua-long, GAO Shang
摘要： 传统的推荐算法着重于提高推荐结果的精确度,对于推荐列表的多样性则有所忽略。但很多研究表明,用户对产品的多样性需求也是影响用户体验的重要因素之一。针对该问题,在用户覆盖定义的基础上,提出了一个基于产品评分差异的用户覆盖模型。在生成用户兴趣域(用户覆盖)的过程中,该模型一方面通过构建评分差异矩阵,将不同用户对同一产品评分上的差异与用户覆盖模型有效地结合起来,从而计算得到更精准的用户兴趣域;另一方面,该模型将用户和推荐列表的兴趣域分别映射到两个m维向量上(分别称为用户向量和产品集向量),并将推荐的目标函数向量化,有效地减少了计算过程中迭代的次数。此外,通过探讨用户向量和产品集向量之间的相似度关系,提出了一种新的推荐列表选取策略。基于该方法构建得到的模型可以在一定程度上兼顾推荐精确度与多样性。由于根据用户向量构建匹配的产品集向量是一个NP-hard问题,因此使用贪心算法来求解该问题,贪心算法的有界性有着严谨的理论依据。在两个真实的数据集上进行实验,结果表明,与多样性推荐方面相关的先进算法相比,所提算法具有一定的优势。
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