Mining usage patterns in residential intranet of things

International audienceUbiquitous smart technologies gradually transform modern homes into Intranet of Things, where a multitude of connected devices allow for novel home automation services (e.g., energy or bandwidth savings, comfort enhancement, etc.). Optimizing and enriching the Quality of Experience (QoE) of residential users emerges as a critical differentiator for Internet and Communication Service providers (ISPs and CSPs, respectively) and heavily relies on the analysis of various kinds of data (connectivity, performance , usage) gathered from home networks. In this paper, we are interested in new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous works, to analyze individual device logs of home gateways. Based on multidimensional patterns mining framework, we extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Such fine-grained device usage patterns provide valuable insights for emerging use cases such as an adaptive usage of home devices, and also " things " recommendation

Modeling Viewer and Influencer Behavior on Streaming Platforms

The video streaming industry is growing rapidly, and consumers are increasingly using ad-supported streaming services. There are important questions related to the effect of ad schedules and video elements on viewer behavior that have not been adequately studied in the marketing literature. In my dissertation, I study these topics by applying causal and/or interpretable machine learning methods on behavioral data. In the first essay, “Finding the Sweet Spot: Ad Scheduling on Streaming Media”, I design an “optimal” ad schedule that balances the interest of the viewer (watching content) with that of the streaming platform (ad exposure). This is accomplished using a three-stage approach applied on a dataset of Hulu customers. In the first stage, I develop two metrics – Bingeability and Ad Tolerance – to capture the interplay between content consumption and ad exposure in a viewing session. Bingeability represents the number of completely viewed unique episodes of a show, while Ad Tolerance represents the willingness of a viewer to watch ads and subsequent content. In the second stage, I predict the value of the metrics for the next viewing session using the machine learning method – Extreme Gradient Boosting – while controlling for the non-randomness in ad delivery to a focal viewer using “instrumental variables” based on ad delivery patterns to other viewers. Using “feature importance analyses” and “partial dependence plots” I shed light on the importance and nature of the non-linear relationship with various feature sets, going beyond a purely black-box approach. Finally, in the third stage, I implement a novel constrained optimization procedure built around the causal predictions to provide an “optimal” ad-schedule for a viewer, while ensuring the level of ad exposure does not exceed her predicted Ad Tolerance. Under the optimized schedule, I find that “win-win” schedules are possible that allow for both an increase in content consumption and ad exposure. In the second essay, “Video Influencers: Unboxing the Mystique”, I study the relationship between advertising content in YouTube influencer videos (across text, audio and images) and marketing outcomes (views, interaction rates and sentiment). This is accomplished with the help of novel interpretable deep-learning architectures that avoid making a trade-off between predictive ability and interpretability. Specifically, I achieve high predictive performance by avoiding ex-ante feature engineering and achieve better interpretability by eliminating spurious relationships confounded by factors unassociated with “attention” paid to video elements. The attention mechanism in the Text and Audio models along with gradient maps in the Image model allow identification of video elements on which attention is paid while forming an association with an outcome. Such an ex-post analysis allows me to find statistically significant relationships between video elements and marketing outcomes that are supplemented by a significant increase in attention to video elements. By eliminating spurious relationships, I generate hypotheses that are more likely to have causal effects when tested in a field setting. For example, I find that mentioning a brand in the first 30 seconds of a video is on average associated with a significant increase in attention to the brand but a significant decrease in sentiment expressed towards the video. Overall, my dissertation provides solutions and identifies strategies that can improve the welfare of viewers, platform owners, influencers and brand partners. Policy makers also stand to gain from understanding the power exerted by different stakeholders over viewer behavior.PHDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169824/1/prajaram_1.pd

Deliverable D4.4 User profile and contextual adaptation

This deliverable presents the methods employed in LinkedTV to create, update and formalise a semantic user model. In addition, the first approach on extraction of context and contextual features and its adaptation onto the semantic user profiles is presented

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing