AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems
AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems
复制标题
AR-CF：在协同过滤中增强虚拟用户和项目以解决冷启动问题
DOI：
10.1145/3397271.3401038
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Sang
中科院分区：
文献类型：
--
作者：
Dong;Jihoo Kim;Duen Horng Chau;Sang
冷启动问题可以说是推荐系统中使用的协同过滤（CF）所面临的最大挑战。当可用的评级很少时，CF模型通常不能为冷启动用户提供令人满意的推荐，或者不能在用户的前N个推荐列表上显示冷启动项目。数据填补一直是一个流行的选择，以处理这些问题的背景下，CF，填补空白的评级与推断的分数。不同于（和补充）数据插补，本文提出了AR-CF，这代表增强现实CF，一种新的框架，用于解决冷启动问题，通过生成虚拟的，但合理的邻居冷启动用户或项目，并将它们作为CF模型的附加信息的评级矩阵。值得注意的是，AR-CF不仅直接解决了冷启动问题，而且还有效地提高了整体推荐质量。通过对真实世界数据集的大量实验，AR-CF被证明（1）显着提高了冷启动用户的推荐准确性，（2）提供了有意义的数量的冷启动项目显示在前N个用户列表中，（3）在基本的前N个推荐中也实现了最佳的准确性，所有这些都与最近的最先进的方法进行了比较。
Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF) used in recommender systems. When few ratings are available, CF models typically fail to provide satisfactory recommendations for cold-start users or to display cold-start items on users' top-N recommendation lists. Data imputation has been a popular choice to deal with such problems in the context of CF, filling empty ratings with inferred scores. Different from (and complementary to) data imputation, this paper presents AR-CF, which stands for Augmented Reality CF, a novel framework for addressing the cold-start problems by generating virtual, but plausible neighbors for cold-start users or items and augmenting them to the rating matrix as additional information for CF models. Notably, AR-CF not only directly tackles the cold-start problems, but is also effective in improving overall recommendation qualities. Via extensive experiments on real-world datasets, AR-CF is shown to (1) significantly improve the accuracy of recommendation for cold-start users, (2) provide a meaningful number of the cold-start items to display in top-N lists of users, and (3) achieve the best accuracy as well in the basic top-N recommendations, all of which are compared with recent state-of-the-art methods.