Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based on this quantification. Our approach, called UniCoRn (Unifying Counterfactual Rankings), provides explicit control over the quality of the experiment and its computation cost. Further, we prove that our experiment design is optimal to the proposed design quality measure. Our approach is agnostic to the density of the producer-consumer network and does not rely on any treatment propagation assumption. Moreover, unlike the existing approaches, we do not need to know the underlying network in advance, making this widely applicable to the industrial setting where the underlying network is unknown and challenging to predict a priori due to its dynamic nature. We use simulations to validate our approach and compare it against existing methods. We also deployed UniCoRn in an edge recommendation application that serves tens of millions of members and billions of edge recommendations daily.

Social networks act as major content marketplaces where creators and consumers come together to share and consume various kinds of content. Content ranking applications (e.g., newsfeed, moments, notifications) and edge recommendation products (e.g., connect to members, follow celebrities or groups or hashtags) on such platforms aim at improving the consumer experience. In this work, we focus on the creator experience and specifically on improving edge recommendations to better serve creators in such ecosystems.

Social media platforms bring together content creators and content consumers through recommender systems like newsfeed. The focus of such recommender systems has thus far been primarily on modeling the content consumer preferences and optimizing for their experience. However, it is equally critical to nurture content creation by prioritizing the creators' interests, as quality content forms the seed for sustainable engagement and conversations, bringing in new consumers while retaining existing ones. In this work, we propose a modeling approach to predict how feedback from content consumers incentivizes creators. We then leverage this model to optimize the newsfeed experience for content creators by reshaping the feedback distribution, leading to a more active content ecosystem. Practically, we discuss how we balance the user experience for both consumers and creators, and how we carry out online A/B tests with strong network effects. We present a deployed use case on the LinkedIn newsfeed, where we used this approach to improve content creation significantly without compromising the consumers' experience.

This poster presents a distributed model-based fault detection algorithm which is based on local pair-wise verification. We first show that there exists a linear relationship between the outputs of any pair of sensors. Therefore, a network can be partitioned into sensor pairs, and the relationship between a pair of sensors can be modeled by a linear model. In addition to detecting general faults happened within a sensor pair, we developed an algorithm for identifying non-linearity type of fault without the use of reference sensors. Preliminary performance analysis shows that this scalable algorithm achieves high diagnosis accuracy. Communication power is also greatly reduced by the distributed nature of the algorithm.

This paper introduces a novel use of concepts from combinatorial group testing and Kalman filtering in detecting faulty sensors in a network when faults are relatively rare. By assigning sensors to specific groups and performing Kalman filter-based fault detection over these groups, we can obtain a small binary detection outcome, which can be decoded to reveal the fault state of all sensors in the network. Compared to existing methods, our algorithm achieves similar or better detection accuracy with fewer tests and thus lower computational complexity. We perform extensive numerical analysis using a set of real vibration data collected from the New Carquinez Bridge in California using an 18-sensor network mounted on the bridge.

This poster presents a distributed reference-free fault detection algorithm which is based on local pair-wise verification. We show there exist a linear relationship between the output of any pair of sensors if the system excitations can be aggregated as a single system input. Using this relationship, faulty sensors suffering from sparse spike errors can be identified with high accuracy by our algorithm. An appealing feature of our method is that existence of reference sensors and knowledge of system input are not required. preliminary performance analysis shows that the algorithm is scalable, robust and able to detect most of the faults exist in the sensors. Communication power is also greatly reduced by the distributed nature of the algorithm.