Collaborative filtering is widely used in recommendation systems. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e., provide sufficient ratings. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a tradeoff between the rating cost and the recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated ratings are used for generating recommendations, we design a behavior rule which allows users to achieve an SE via iteratively rating items. We theoretically analyze under what conditions an SE can be learned via the behavior rule. Experimental results on Jester and MovieLens data sets confirm the analysis and demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.

中文翻译：

---

用户参与基于协同过滤的推荐系统：博弈论方法


协同过滤广泛应用于推荐系统中。用户只有自己和其他用户都积极参与，即提供足够的评分，才能获得高质量的推荐。然而，由于评级成本，理性用户倾向于提供尽可能少的评级。因此，评级成本和推荐质量之间存在权衡。在本文中，我们将用户之间的交互建模为满意度形式的博弈，并研究相应的均衡，即满意度均衡（SE）。考虑到累积的评分用于生成推荐，我们设计了一个行为规则，允许用户通过迭代评分项目来实现SE。我们从理论上分析了在什么条件下可以通过行为规则来学习SE。 Jester和MovieLens数据集上的实验结果证实了该分析，并表明，如果所有用户对推荐质量都有适度的期望，并且满意的用户愿意提供更多的评分，那么所有用户都可以在不提供太多评分的情况下获得满意的推荐。本文对所提出的游戏进行 SE 分析有助于设计鼓励用户参与的机制。