论文标题
通过无偏见和首选的项目序列进行学习，以进行强有力的建议（扩展摘要）
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
论文作者
论文摘要
本文是[Burashnikova等，2021，Arxiv：2012.06910]的扩展版本，我们在其中提出了一种理论支持的顺序策略，用于训练大规模推荐系统（RS）而不是隐式反馈，主要是以点击形式。所提出的方法包括最大程度地减少由一系列非点击项目构成的连续项目的块上的成对排名损失，然后为每个用户单击一个。我们提出了该策略的两个变体，其中使用动量方法或基于梯度的方法更新模型参数。为了防止对某些目标项目（主要是由于机器人）的异常点击更新参数，我们在每个用户的更新次数上引入了较高和较低的阈值。这些阈值估计在训练集中的块数量分布上。它们通过转移向用户显示的项目的分布来影响RS的决定。此外，我们提供了两种算法的收敛分析，并在各种排名措施方面证明了它们在六个大规模集合中的实践效率。
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012.06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks. The proposed approach consists in minimizing pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user. We present two variants of this strategy where model parameters are updated using either the momentum method or a gradient-based approach. To prevent updating the parameters for an abnormally high number of clicks over some targeted items (mainly due to bots), we introduce an upper and a lower threshold on the number of updates for each user. These thresholds are estimated over the distribution of the number of blocks in the training set. They affect the decision of RS by shifting the distribution of items that are shown to the users. Furthermore, we provide a convergence analysis of both algorithms and demonstrate their practical efficiency over six large-scale collections with respect to various ranking measures.
