338 research outputs found
When Social Influence Meets Item Inference
Research issues and data mining techniques for product recommendation and
viral marketing have been widely studied. Existing works on seed selection in
social networks do not take into account the effect of product recommendations
in e-commerce stores. In this paper, we investigate the seed selection problem
for viral marketing that considers both effects of social influence and item
inference (for product recommendation). We develop a new model, Social Item
Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we
formulate a seed selection problem, called Social Item Maximization Problem
(SIMP), and prove the hardness of SIMP. We design an efficient algorithm with
performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and
develop a new index structure, called SIG-index, to accelerate the computation
of diffusion process in HAG. Moreover, to construct realistic SIG models for
SIMP, we develop a statistical inference based framework to learn the weights
of hyperedges from data. Finally, we perform a comprehensive evaluation on our
proposals with various baselines. Experimental result validates our ideas and
demonstrates the effectiveness and efficiency of the proposed model and
algorithms over baselines.Comment: 12 page
Modeling Interdependent and Periodic Real-World Action Sequences
Mobile health applications, including those that track activities such as
exercise, sleep, and diet, are becoming widely used. Accurately predicting
human actions is essential for targeted recommendations that could improve our
health and for personalization of these applications. However, making such
predictions is extremely difficult due to the complexities of human behavior,
which consists of a large number of potential actions that vary over time,
depend on each other, and are periodic. Previous work has not jointly modeled
these dynamics and has largely focused on item consumption patterns instead of
broader types of behaviors such as eating, commuting or exercising. In this
work, we develop a novel statistical model for Time-varying, Interdependent,
and Periodic Action Sequences. Our approach is based on personalized,
multivariate temporal point processes that model time-varying action
propensities through a mixture of Gaussian intensities. Our model captures
short-term and long-term periodic interdependencies between actions through
Hawkes process-based self-excitations. We evaluate our approach on two activity
logging datasets comprising 12 million actions taken by 20 thousand users over
17 months. We demonstrate that our approach allows us to make successful
predictions of future user actions and their timing. Specifically, our model
improves predictions of actions, and their timing, over existing methods across
multiple datasets by up to 156%, and up to 37%, respectively. Performance
improvements are particularly large for relatively rare and periodic actions
such as walking and biking, improving over baselines by up to 256%. This
demonstrates that explicit modeling of dependencies and periodicities in
real-world behavior enables successful predictions of future actions, with
implications for modeling human behavior, app personalization, and targeting of
health interventions.Comment: Accepted at WWW 201
Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations
The rapid adoption of wireless sensor devices has made it easier to record location information of people in a variety of spaces (e.g., exhibition halls). Location information is often aggregated due to privacy and/or cost concerns. The aggregated data we use as input consist of the numbers of incoming and outgoing people at each location and at each time step. Since the aggregated data lack tracking information of individuals, determining the flow of people between locations is not straightforward. In this article, we address the problem of inferring latent people flows, that is, transition populations between locations, from just aggregated population data gathered from observed locations. Existing models assume that everyone is always in one of the observed locations at every time step; this, however, is an unrealistic assumption, because we do not always have a large enough number of sensor devices to cover the large-scale spaces targeted. To overcome this drawback, we propose a probabilistic model with flow conservation constraints that incorporate travel duration distributions between observed locations. To handle noisy settings, we adopt noisy observation models for the numbers of incoming and outgoing people, where the noise is regarded as a factor that may disturb flow conservation, e.g., people may appear in or disappear from the predefined space of interest. We develop an approximate expectation-maximization (EM) algorithm that simultaneously estimates transition populations and model parameters. Our experiments demonstrate the effectiveness of the proposed model on real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City
Sociophysics Analysis of the Dynamics of Peoples' Interests in Society
As a method of analyzing and predicting social phenomena using social media as data, we present analyses based on the mathematical model of the hit phenomenon, which is one of the established models of sociophysics. The dynamics of the number of social media posts for movies, events, and a YouTube movie are explained. For entertainment topics, the direct communication strength, “D,” indicates the satisfaction of the current interested people or supporters, whereas the indirect communication strength, “P,” indicates the power to acquire a new support layer. Thus, this is effective not only for the analysis of entertainment and marketing strategy but also for burst analysis on the social media
Essays on Mobile Channel User Behavior
abstract: In two independent and thematically relevant chapters, I empirically investigate consumers’ mobile channel usage behaviors. In the first chapter, I examine the impact of mobile use in online higher education. With the prevalence of affordable mobile devices, higher education institutions anticipate that learning facilitated through mobile access can make education more accessible and effective, while some critics of mobile learning worry about the efficacy of small screens and possible distraction factors. I analyze individual-level data from Massive Open Online Courses. To resolve self-selection issues in mobile use, I exploit changes in the number of mobile-friendly, short video lectures in one course (“non-focal course”) as an instrumental variable for a learner’s mobile intensity in the other course (“focal course”), and vice versa, among learners who have taken both courses during the same semester. Results indicate that high mobile intensity impedes, or at most does not improve course engagement due mainly to mobile distractions from doing activities unrelated to learning. Finally, I discuss practical implications for researchers and higher education institutions to improve the effectiveness of mobile learning. In the second chapter, I investigate the impact of mobile users’ popular app adoption on their app usage behaviors. The adoption of popular apps can serve as a barrier to the use of other apps given popular apps’ addictive nature and users’ limited time resources, while it can stimulate the exploration of other apps by inspiring interest in experimentation with similar technologies. I use individual-level app usage data and develop a joint model of the number of apps used and app usage duration. Results indicate that popular app adoption stimulates users to explore new apps at app stores and allocate more time to them such that it increases both the number of apps used and app usage duration for apps excluding the popular app. Such positive spillover effects are heterogeneous across app categories and user characteristics. I draw insights for app developers, app platforms, and media planners by determining which new apps to release in line with the launch of popular apps, when to release such apps, and to whom distribution should be targeted.Dissertation/ThesisDoctoral Dissertation Business Administration 201
Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation
Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice
THE INFLUENCE OF ADVERTISING APPROACH IN A TRIAL PURCHASE CONTEXT
Research investigating the development of trial purchase has recognised that the chosen advertising
approach is a key influence in the trial process. This thesis describes a critique of the central and
related literature surrounding the influence of advertising approach in a trial purchase context, with
particular emphasis on the concept of disruptive advertising. This concept can be described as an
overturning of convention for commercial benefit, and has been introduced to deal with change.
Whilst it is clear that many brands achieve success via a consistent approach to advertising, it is also
true that successful advertising is often rooted in 'doing something different'.
The literature reveals that there is a lack of empirical work to date on brand/market situations in which
a disruptive approach to advertising will be more appropriate and successful than a conventional
approach at stimulating purchase or perhaps increasing awareness of a brand. In which situations
should a disruptive advertising approach be applied? Phase 1 of the research investigates the possible
link between a brand situation, the advertising strategy adopted and the brand success using existing
advertising case materials. Focus group interviews are then utilised in phase 2 to gain some
understanding of consumer attitudes towards different advertising approaches in different product
markets and also to explore brand usage and brand choices in these markets. Content analysis is
applied to the results of phases 1 and 2. Finally. phase 3 of the research more specifically investigates
the influence of advertising approach on purchase intention within different product markets compared
with other primary influencing factors. A questionnaire survey was administered to undergraduate
students at the University of Plymouth for phase 3 and the results were analysed using individual item
analysis and multiple regression.
A generic model of 'The Influence of Advertising Approach in a Trial Purchase Context' is
constructed from the literature and a modified version is used to discuss the results of the study. The
results indicate that the choice of a disruptive or conventional advertising approach does affect
intention to purchase a brand and that the nature of product involvement does influence intention to
purchase a brand. However, the research has not been able to provide conclusive evidence as to the
situations in which a disruptive advertising approach should be applied and consequently little can be
recommended to managers regarding disruption on an operational basis. In addition, the study has
provided little evidence to support the concept of disruption other than as an elaborate repackaging of
positioning theory. Additional research using non-student populations and a greater selection of low
involvement and high involvement markets is recommended, however, in order to validate the
relationships found
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