论文标题
通过内容创建建模在推荐系统中长期用户价值优化
Long-run User Value Optimization in Recommender Systems through Content Creation Modeling
论文作者
论文摘要
内容推荐系统通常擅长最大化立即的用户满意度，但要优化\ textIt {Long-Run}用户价值，我们需要比现成的简单建议算法更统计上复杂的解决方案。在本文中，我们制定了这样的解决方案，通过折扣实用程序最大化和一种用于估算它的机器学习方法来优化\ textit {Long-Run}用户价值。我们的方法估算哪些内容生产商最有可能创建最高的长期用户价值，如果将其内容显示给当前喜欢的用户。我们在A/B测试和异质效应机器学习模型的帮助下进行了此估计。我们已经在Facebook的Feed排名系统中使用了此类模型，并且可以在其他推荐系统中使用此类模型。
Content recommender systems are generally adept at maximizing immediate user satisfaction but to optimize for the \textit{long-run} user value, we need more statistically sophisticated solutions than off-the-shelf simple recommender algorithms. In this paper we lay out such a solution to optimize \textit{long-run} user value through discounted utility maximization and a machine learning method we have developed for estimating it. Our method estimates which content producers are most likely to create the highest long-run user value if their content is shown more to users who enjoy it in the present. We do this estimation with the help of an A/B test and heterogeneous effects machine learning model. We have used such models in Facebook's feed ranking system, and such a model can be used in other recommender systems.
