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Peter Gorniak

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2 papers

2 author rows

AAAI Conference 2016 Conference Paper

Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization

Xin Wang
Roger Donaldson
Christopher Nell
Peter Gorniak
Martin Ester
Jiajun Bu

Social networks often provide group features to help users with similar interests associate and consume content together. Recommending groups to users poses challenges due to their complex relationship: user-group afﬁnity is typically measured implicitly and varies with time; similarly, group characteristics change as users join and leave. To tackle these challenges, we adapt existing matrix factorization techniques to learn user-group afﬁnity based on two different implicit engagement metrics: (i) which group-provided content users consume; and (ii) which content users provide to groups. To capture the temporally extended nature of group engagement we implement a time-varying factorization. We test the assertion that latent preferences for groups and users are sparse in investigating elastic-net regularization. Our experiments indicate that the time-varying implicit engagement-based model provides the best top-K group recommendations, illustrating the beneﬁt of the added model complexity.

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UAI Conference 2000 Conference Paper

Building a Stochastic Dynamic Model of Application Use

Peter Gorniak
David Poole 0001

Many intelligent user interfaces employ application and user models to determine the user's preferences, goals and likely future actions. Such models require application analysis, adaptation and expansion. Building and maintaining such models adds a substantial amount of time and labour to the application development cycle. We present a system that observes the interface of an unmodified application and records users' interactions with the application. From a history of such observations we build a coarse state space of observed interface states and actions between them. To refine the space, we hypothesize sub-states based upon the histories that led users to a given state. We evaluate the information gain of possible state splits, varying the length of the histories considered in such splits. In this way, we automatically produce a stochastic dynamic model of the application and of how it is used. To evaluate our approach, we present models derived from real-world application usage data.

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