计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 78-83.doi: 10.11896/j.issn.1002-137X.2019.08.012
张艳红1, 张春光2, 周湘贞3, 王怡鸥4
ZHANG Yan-hong1, ZHANG Chun-guang2, ZHOU Xiang-zhen3, WANG Yi-ou4
摘要: 针对视频协同过滤推荐算法多样性较低的问题,提出了一种基于多属性联合的多样性视频协同过滤推荐算法。根据用户与推荐系统的互动历史记录,判断用户是否满意系统的推荐项目,如果某个用户过去观看同一个主题的视频节目,并且不关心视频的作者,那么认为该用户对视频作者表现出较高的多样性,对视频节目主题表现出的多样性较低。采用信息熵与用户配置信息长度两个指标来评估项目各个属性的多样性,根据两个指标的组合将用户对每个项目属性的多样性分为4个象限,并且对用户多样性进行模糊化处理,以获得用户多样性对于4个象限的隶属度。在第一个阶段预测未评分项目的评分;在第二个阶段将所有项目重新排序,以提高推荐列表的多样性。最终,基于公开的Movielens 1M数据集进行了对比实验,实验结果证明本算法可实现接近top-N算法的准确率性能,同时具有一定的多样性增强效果。在推荐准确率与多样性平衡的应用场景下,设置合适的参数能够在损失较少推荐准确率的前提下,显著提高个体多样性、总体多样性与新颖性。
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