In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback. We propose an online algorithm, where model parameters are updated user per user over blocks of items constituted by a sequence of unclicked items followed by a clicked one. We illustrate through thorough empirical evaluations that filtering users with respect to the degree of long memory contained in their interactions with the system allows to substantially gain in performance with respect to MAP and NDCG, especially in the context of training large-scale Recommender Systems.

翻译：在本文中,我们研究了长期记忆对学习连续推荐系统的影响,包括用户的隐含反馈;我们提议了在线算法,其中模型参数由每个用户在一组项目上更新用户,这些项目由一系列未点击的项目组成,然后点击一个项目;我们通过透彻的经验评价来说明,过滤用户在与系统互动过程中所包含长期记忆的程度,可以大大提高MAP和NDCG的性能,特别是在培训大型建议系统方面。