One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
South China University of Technology（华南理工大学）；JD Finance America Corporation（京东金融美国公司）；Peng Cheng Laboratory（鹏城实验室）；Boshi Qiangzhi Science and Technology Co., Ltd（博时强智科技有限公司）
FedFlex: Federated Learning for Diverse Netflix Recommendations
Vrije Universiteit Amsterdam Netherlands；Georgia Institute of Technology Mexico；Centrum Wiskunde \& Informatica Netherlands；Georgia Institute of Technology
Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks
Stanford University（斯坦福大学）；University of Zürich（苏黎世大学）
大多数现有的个性化系统推广与用户先前的选择相匹配的项目或在类似用户中流行的项目。这会导致建议与用户已经接触到的建议高度相似，导致他们被隔离在熟悉但绝缘的信息孤岛中。在这种情况下，我们开发了一种新的推荐框架，其目标是使用修改后的用户项图的随机游走探索来提高信息多样性。我们专注于政治内容推荐的问题，同时解决适用于其他社会和信息网络的个性化任务的一般问题。为了在社交网络上推荐政治内容，我们首先提出了一个新的模型来估计用户和他们分享的内容的意识形态立场，该模型能够以高精度恢复意识形态立场。基于这些估计的位置，我们产生多样化的个性化推荐使用我们的新的随机行走为基础的推荐算法。通过对Twitter讨论的大数据集的实验评估，我们表明，我们的方法基于\emph{random walks with erasure}是能够产生更多的意识形态多样化的建议。我们的方法并不依赖于用户或内容制作者的偏见标签的可用性。通过在其他社交和信息网络的开放基准数据集上的实验，我们还证明了我们的方法在推荐不同长尾项目方面的有效性。
