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  1. Recommender systems are widely used to help customers find the most relevant and personalized products or services tailored to their preferences. However, traditional systems ignore the preferences of the other side of the market, e.g., “product suppliers” or “service providers”, towards their customers. In this paper, we present 2SRS a Two-Sided Recommender System that recommends coupons, supplied by local businesses, to passerby while considering the preferences of both sides towards each other. For example, some passerby may only be interested in coffee shops whereas certain businesses may only be interested in sending coupons to new customers only. Our experimental resultsmore »show that 2SRS delivers higher satisfaction when considering both sides of the market compared to the baseline methods.« less
  2. Public transit is one of the first things that come to mind when someone talks about “smart cities.” As a result, many technologies, applications, and infrastructure have already been deployed to bring the promise of the smart city to public transportation. Most of these have focused on answering the question, “When will my bus arrive?”; little has been done to answer the question, “How full will my next bus be?” which also dramatically affects commuters’ quality of life. In this article, we consider the bus fullness problem. In particular, we propose two different formulations of the problem, develop multiple predictivemore »models, and evaluate their accuracy using data from the Pittsburgh region. Our predictive models consistently outperform the baselines (by up to 8 times).« less
  3. The pervasiveness of public displays is prompting an increased need for “fresh” content to be shown, that is highly engaging and useful to passerbys. As such, live or time-sensitive content is often shown in conjunction with “traditional” static content, which creates scheduling challenges. In this work, we propose a utility-based framework that can be used to represent the usefulness of a content item over time. We develop a novel scheduling algorithm for handling live and non-live content on public displays using our utility-based framework. We experimentally evaluate our proposed algorithm against a number of alternatives under a variety of workloads;more »the results show that our algorithm performs well on the proposed metrics. Additional experimental evaluation shows that our utility-based framework can handle changes in priorities and deadlines of content items, without requiring any involvement by the display owner beyond the initial setup.« less
  4. In this paper, we describe the implementation of an information sharing platform, got-toilet-paper.com. We create this web page in response to the COVID-19 pandemic to help the Pittsburgh, PA community share information about congestion and product shortages in supermarkets. We show that the public good problem of the platform makes it difficult for the platform to operate. In particular, there is sizable demand for the information, but supply satis es only a small fraction of demand. We provide a theoretical model and show that the first best outcomes cannot be obtained in a free market and the best symmetric equilibrium outcomemore »decreases as the number of participant increases. Also, the best symmetric equilibrium has two problems, cost inefficiency and positive probability of termination. We discuss two potential solutions. The first is a uniform random sharing mechanism, which implies randomly selecting one person every period who will be responsible for information sharing. It is ex-post individually rational but hard to implement. The second solution is the one that we began implementing. It implies selecting a person at the beginning and make her responsible to share information every period, while reimbursing her cost. We discuss the reasons for high demand and low supply both qualitatively and quantitatively.« less
  5. The pervasiveness of public displays is prompting an increased need for "fresh" content to be shown, that is highly engaging and useful to passerbys. As such, live or time-sensitive content is often shown in conjunction with "traditional" static content, which creates scheduling challenges. In this work, we propose a utility-based framework and a novel scheduling algorithm for handling live and non-live content on public displays. We also experimentally evaluate our proposed algorithm against a number of alternatives under a variety of workloads.
  6. During recent years there have been several efforts from city and transportation planners, as well as, port authorities, to design multimodal transport systems, covering the needs of the population to be served. However, before designing such a system, the first step is to understand the current gaps. Does the current system meet the transit demand of the geographic area covered? If not, where are the gaps between supply and demand? To answer this question, the notion of transit desert has been introduced. A transit desert is an area where the supply of transit service does not meet the demand formore »it. While there is little ambiguity on what constitutes transit demand, things are more vague when it comes to transit supply. Existing efforts often define transit supply using volume metrics (e.g., number of bus stops within a pre-defined distance). However, this does not necessarily capture the quality of the transit service. In this study, we introduce a network-based transit desert index (which we call TDI) that captures not only the quantity of transit supply in an area, but also the connectivity that the transit system provides for an area within the region of interest. In particular, we define a network between areas based on the transit travel time, distance, and overall quantity of connections. We use these measures to examine two notions of transit quality: connectivity and availability. To quantify the connectivity of an area i we utilize the change observed in the second smallest eigenvalue of the Laplacian when we remove node i from the network. To quantify availability of an area i, we examine the number of routes which pass through this area as given by an underlying transit network. We further apply and showcase our approach with data from Allegheny County, Pennsylvania, USA. Finally, we discuss current limitations of TDI and how we can tackle them as part of our future research.« less
  7. The amount of food waste generated by the U.S. is staggering, both expensive in economic cost and environmental side effects. Surplus food, which could be used to feed people facing food insecurity, is instead discarded and placed in landfills. Institutions, universities, and non-profits have noticed this issue and are beginning to take action to reduce surplus food waste, typically by redirecting it to food banks and other organizations or having students transport or eat the food. These approaches present challenges such as transportation, volunteer availability, and lack of prioritization of those in need. In this paper, we introduce PittGrub, amore »notification system to intelligently select users to invite to events that have leftover food. PittGrub was invented to help reduce food waste at the University of Pittsburgh. We use reinforcement learning to determine how many notifications to send out and a valuation model to determine whom to prioritize in the notifications. Our goal is to produce a system that prioritizes feeding students in need while simultaneously eliminating food waste and maintaining a fair distribution of notifications. As far as we are aware, PittGrub is unique in its approach to eliminating surplus food waste while striving for social good. We compare our proposed techniques to multiple baselines on simulated datasets to demonstrate effectiveness. Experimental results among various algorithms show promise in eliminating food waste while helping those facing food insecurity and treating users fairly. Our prototype is currently in beta and coming soon to the Apple App Store.« less
  8. The increasing popularity and ubiquity of various large graph datasets has caused renewed interest for graph partitioning. Existing graph partitioners either scale poorly against large graphs or disregard the impact of the underlying hardware topology. A few solutions have shown that the nonuniform network communication costs may affect the performance greatly. However, none of them considers the impact of resource contention on the memory subsystems (e.g., LLC and Memory Controller) of modern multicore clusters. They all neglect the fact that the bandwidth of modern high-speed networks (e.g., Infiniband) has become comparable to that of the memory subsystems. In this paper,more »we provide an in-depth analysis, both theoretically and experimentally, on the contention issue for distributed workloads. We found that the slowdown caused by the contention can be as high as 11x. We then design an architecture-aware graph partitioner, ARGO , to allow the full use of all cores of multicore machines without suffering from either the contention or the communication heterogeneity issue. Our experimental study showed (1) the effectiveness of ARGO , achieving up to 12x speedups on three classic workloads: Breadth First Search, Single Source Shortest Path, and PageRank; and (2) the scalability of ARGO in terms of both graph size and the number of partitions on two billion-edge real-world graphs.« less