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What to learn next: recommending commands in a feature-rich environment

University of British Columbia

Despite an abundance of commands to make tasks easier to perform, the users of feature-rich applications, such as development environments and AutoCAD applications, use only a fraction of the commands available due to a lack of awareness of the existence of many commands. Earlier work has shown that command recommendation can improve the usage of a range of commands available within such applications. In this thesis, we address the command recommendation problem, in which, given the command usage history of a set of users, the objective is to predict a command that is likely useful for the user to learn. We investigate two approaches to address the problem. The first approach is built upon the hypothesis that users of feature-rich applications who have similar features tend to use the same commands, and also, a specific user tends to use commands with similar features. Building on this hypothesis, we describe a supervised learning framework that exploits features from a user-command network to predict new links among users and commands. The second approach is built upon three hypotheses. First, we hypothesize that in feature-rich applications there exists co-occurrence patterns between commands. Second, we hypothesize that users of feature-rich applications have prevalent discovery patterns. Finally, we hypothesize that users need different recommendations based on the time elapsed between their last activity and the time of recommendation. To generate recommendations, we obtain co-occurrence and discovery patterns from the command usage history of a large set of users of the same feature-rich application. Subsequently, for each user, we produce recommendations based on the user's command usage history, co-occurrence and discovery patterns, and time elapsed since the last command usage. We refer to the algorithm we developed according to this approach as CoDis. Empirical experiments on data submitted by users of an integrated development environment (Eclipse) demonstrate that CoDis achieves significant performance improvements over link prediction, standard algorithms used for command recommendation, and matrix factorization techniques that are known to perform well in other domains. Compared to ADAGRAD, the best performing baseline, it achieves an improvement of 10.22% in recall, for a top-N recommendation task (N=20).

Text

2016-01-31

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/54362

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/