Advanced collaborative filtering methods based on explicit feedback assume that unknown ratings are missing not at random. The state-of-the-art algorithm hypothesizes that unknown items are weakly rated and sets an explicit prior to unknown ratings. However, the prior assuming unknown ratings be close to zero may be questionable and it is challenging to set appropriate prior ratings for unknown items. In this article, to avert the use of prior ratings, we propose a ranking-based prior by hypothesizing that each user's unknown ratings are close to each other. This prior essentially acts as a regularizer to penalize the discrepancy of predicted ratings between any two unknown items. With the ranking-based prior, we design a generic collaborative filtering framework for explicit feedback and develop an efficient optimization algorithm for parameter learning. We finally evaluate the proposed algorithms on four real-world rating datasets. The results show that the proposed algorithms consistently outperform the state-of-the-art baselines and that the ranking-based prior leads to superior recommendation accuracy.

中文翻译：

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基于未知评级的基于排名的先验的协同过滤

基于显式反馈的高级协同过滤方法假设未知评级不是随机丢失的。最先进的算法假设未知项目的评级很弱，并在未知评级之前设置显式。然而，先验假设未知评级接近于零可能是有问题的，并且为未知项目设置适当的先验评级具有挑战性。在本文中，为了避免使用先验评级，我们通过假设每个用户的未知评级彼此接近，提出了基于排名的先验。该先验本质上充当正则化器，以惩罚任何两个未知项目之间预测评分的差异。使用基于排名的先验，我们为显式反馈设计了一个通用的协同过滤框架，并为参数学习开发了一种有效的优化算法。我们最终在四个真实世界的评级数据集上评估了所提出的算法。结果表明，所提出的算法始终优于最先进的基线，并且基于排名的先验导致卓越的推荐准确性。