Analysing user behaviour through dynamic population models

Higgs, Matthew; Morrison, Alistair; Girolami, Mark; Chalmers, Matthew

doi:10.1145/2468356.2468405

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…We first introduced the concept of representing the behaviour of users through a weighted mixture over data gen-erating distributions [11], refining the concept substantially in [1] where we defined activity patterns for an individual user as user meta models (DTMCs), with respect to a population of users. We then inferred behaviours for individual user activity from large scale logged usage data for a mobile game app and analysed them using probabilistic temporal properties (without rewards).…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Formal Analysis of App Usage to Inform Redesign

Andrei

Calder

Chalmers

et al. 2016

Lecture Notes in Computer Science

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This paper sets out a process of app analysis intended to support understanding of use but also redesign. From usage logs we infer activity patterns -Markov models -and employ probabilistic formal analysis to ask questions about the use of the app. The core of this paper's contribution is a bridging of stochastic and formal modelling, but we also describe the work to make that analytic core utile within a design team. We illustrate our work via a case study of a mobile app presenting analytic findings and discussing how they are feeding into redesign. We had posited that two activity patterns indicated two separable sets of users, each of which might benefit from a differently tailored app version, but our subsequent analysis detailed users' interleaving of activity patterns over time -evidence speaking more in favour of redesign that supports each pattern in an integrated way. We uncover patterns consisting of brief glances at particular data and recommend them as possible candidates for new design work on widget extensions: small displays available while users use other apps.

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Section: Related Workmentioning

confidence: 99%

Probabilistic Formal Analysis of App Usage to Inform Redesign

Andrei

Calder

Chalmers

et al. 2016

Lecture Notes in Computer Science

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“…2 we give the activity patterns, inferred from a dataset of user traces for 164 users randomly selected from the user population, for K = 2. A more detailed overview is given in the work-in-progress paper [7]. For brevity, we do not include the exact values of P 1 and P 2 , but thicker arcs correspond to transition probabilities greater than 0.1, thinner ones to transition probabilities in [0.01, 0.1], and dashed ones to transition probabilities smaller than 10 −12 .…”

Section: Example Activity Patterns From Hungry Yoshimentioning

confidence: 99%

Probabilistic Model Checking of DTMC Models of User Activity Patterns

Andrei

Calder

Higgs

et al. 2014

Quantitative Evaluation of Systems

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Software developers cannot always anticipate how users will actually use their software as it may vary from user to user, and even from use to use for an individual user. In order to address questions raised by system developers and evaluators about software usage, we define new probabilistic models that characterise user behaviour, based on activity patterns inferred from actual logged user traces. We encode these new models in a probabilistic model checker and use probabilistic temporal logics to gain insight into software usage. We motivate and illustrate our approach by application to the logged user traces of an iOS app.

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Non‐parametric Bayes to infer playing strategies adopted in a population of mobile gamers

Virtanen

Rost

Higgs

et al. 2015

Stat

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Analysis of trace logging data collections of interactions of a heterogenous and diverse population of consumers of digital software with mobile devices provides unprecedented possibilities for understanding how software is actually used and for finding recurring patterns of software usage over the population that are exhibited to a greater or lesser degree in each individual software user. In this work, we consider an elementary mobile game played by a population of mobile gamers and collect pieces of game sessions over an extended period, resulting in a collection of users' trace logs for multiple sessions. We develop a simple, yet flexible, non-parametric Bayes approach to infer playing strategies adopted in the population from the logged traces of game interactions. We demonstrate that our approach finds interpretable strategies and provides good predictive performance compared with alternative modelling assumptions using a non-parametric Bayes framework.

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Analysing user behaviour through dynamic population models

Cited by 5 publications

References 3 publications

Probabilistic Formal Analysis of App Usage to Inform Redesign

Probabilistic Formal Analysis of App Usage to Inform Redesign

Probabilistic Model Checking of DTMC Models of User Activity Patterns

Non‐parametric Bayes to infer playing strategies adopted in a population of mobile gamers

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