You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction

In the cost per click (CPC) pricing model, an advertiser pays an ad network only when a user clicks on an ad; in turn, the ad network gives a share of that revenue to the publisher where the ad was impressed. Still, advertisers may be unsatisfied with ad networks charging them for "valueless" clicks, or so-called accidental clicks. [...] Charging advertisers for such clicks is detrimental in the long term as the advertiser may decide to run their campaigns on other ad networks. In addition, machine-learned click models trained to predict which ad will bring the highest revenue may overestimate an ad click-through rate, and as a consequence negatively impacting revenue for both the ad network and the publisher. In this work, we propose a data-driven method to detect accidental clicks from the perspective of the ad network. We collect observations of time spent by users on a large set of ad landing pages - i.e., dwell time. We notice that the majority of per-ad distributions of dwell time fit to a mixture of distributions, where each component may correspond to a particular type of clicks, the first one being accidental. We then estimate dwell time thresholds of accidental clicks from that component. Using our method to identify accidental clicks, we then propose a technique that smoothly discounts the advertiser's cost of accidental clicks at billing time. Experiments conducted on a large dataset of ads served on Yahoo mobile apps confirm that our thresholds are stable over time, and revenue loss in the short term is marginal. We also compare the performance of an existing machine-learned click model trained on all ad clicks with that of the same model trained only on non-accidental clicks. There, we observe an increase in both ad click-through rate (+3.9%) and revenue (+0.2%) on ads served by the Yahoo Gemini network when using the latter. [...

Adaptive stochastic radio access selection scheme for cellular-WLAN heterogeneous communication systems

This study proposes a novel adaptive stochastic radio access selection scheme for mobile users in heterogeneous cellular-wireless local area network (WLAN) systems. In this scheme, a mobile user located in dual coverage area randomly selects WLAN with probability of ω when there is a need for downloading a chunk of data. The value of ω is optimised according to the status of both networks in terms of network load and signal quality of both cellular and WLAN networks. An analytical model based on continuous time Markov chain is proposed to optimise the value of ω and compute the performance of proposed scheme in terms of energy efficiency, throughput, and call blocking probability. Both analytical and simulation results demonstrate the superiority of the proposed scheme compared with the mainstream network selection schemes: namely, WLAN-first and load balancing

On the Inability of Markov Models to Capture Criticality in Human Mobility

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish a theoretical upper bound on the predictability of human mobility (expressed as a minimum error probability limit), based on temporally correlated entropy. Since its inception, this bound has been widely used and empirically validated using Markov chains. We show that recurrent-neural architectures can achieve significantly higher predictability, surpassing this widely used upper bound. In order to explain this anomaly, we shed light on several underlying assumptions in previous research works that has resulted in this bias. By evaluating the mobility predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-range correlations, bearing similarity to a power-law decay. This is in contrast to the initial assumption that human mobility follows an exponential decay. This assumption of exponential decay coupled with Lempel-Ziv compression in computing Fano's inequality has led to an inaccurate estimation of the predictability upper bound. We show that this approach inflates the entropy, consequently lowering the upper bound on human mobility predictability. We finally highlight that this approach tends to overlook long-range correlations in human mobility. This explains why recurrent-neural architectures that are designed to handle long-range structural correlations surpass the previously computed upper bound on mobility predictability

Building up knowledge through passive WiFi probes

Inexpensive WiFi-capable hardware can be nowadays easily used to capture traffic from end users and extract knowledge. Such knowledge can be leveraged to support advanced services like user profiling, device classification. We review here the main building blocks to develop a system based on passive WiFi monitors, that is, cheap and viable sniffers which collect data from end devices even without an explicit association to any Wi-Fi network. We provide an overview of the services which can be enabled by such approach with three practical scenarios: user localization, user profiling and device classification. We evaluate the performance of each one of the three scenarios and highlight the challenges and threats for the aforementioned systems

Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions.Comment: 34 pages, 12 figures, 5 table

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications

Understanding and modeling humans and device mobility has fundamental importance in mobile computing, with implications ranging from network design and location-aware technologies to urban infrastructure planning. Today\u27s users carry a plethora of devices such as smartphones, laptops, tablets, and smartwatches, with each device offering a different set of services resulting in different usage and mobility leading to the research question of understanding and modeling multiple user device trajectories. Additionally, prior research on mobility focuses on outdoor mobility when it is known that users spend 80% of their time indoors resulting in wide gaps in knowledge in the area of indoor mobility of users and devices. Here, I try to fill the gaps in mobility modeling in the areas of understanding and modeling indoor-outdoor human mobility as well as multi-device mobility. In this thesis, I propose the characterization and modeling of human and device mobility. Further, I design and deploy mobility-aware applications for contact tracing of infectious diseases and energy-aware Heating, Ventilation, and Air Conditioning (HVAC) scheduling. I try and answer a sequence of four primary inter-related questions : (1) how is indoor and outdoor user mobility different, (2) are multiple device trajectories belonging to a single user correlated, (3) how to model indoor mobility of users and (4) how to design effective mobility aware applications that are easily deployable and align with long term goals of sustainability as well relay positive societal impact. The insights gained from each question serves as a base to build up on the next question in the series. I present answers to these questions across three main parts of my thesis. The first part comprises of characterization and analysis of human and device mobility. In this part I design and develop tool to extract device trajectories from WiFi system logs syslog and map devices to users. These extracted trajectories and device to user mapping are used to characterize and empirically analyze the mobility of users at varying spatial granularity (indoor, outdoor) and extract device mobility correlations between multiple devices of users and forms the first part of my thesis. In the second part, based on the insights gained from the multi-granular and multi-device mobility characterization stated above, I argue that mobility is inherently hierarchical in nature and propose novel indoor human mobility modeling approach. Third, I leverage the passively observed mobility to design mobility-aware applications that either look back or look ahead in time. WiFiTrace is a look back or backtracking application that is a network-centric contact tracing tool to aid healthcare workers in manual contact tracing of infectious diseases and iSchedule is a look ahead machine learning based mobility-aware energy-saving application that predicts Heating, Ventilation, and Air Conditioning (HVAC) schedule for higher energy savings while increasing user comfort