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Feature Recommender : a large-scale in-situ study of proactive software feature recommendations

University of British Columbia

In this thesis, we describe our design of Feature Recommender, a Mozilla Firefox browser extension, which proactively recommends features that it predicts will benefit users based on their individual usage behaviors. The goal of these pop-up notifications is to help users discover new features. How to maximize the effectiveness of such notifications while minimizing user interruptions remains a difficult open problem. One approach is to carefully time when the notifications are delivered. In our deployment of Feature Recommender, we study the effect of two delivery timing parameters: delivery rate and the user's context at the moment of delivery. We also investigate the effect of prediction algorithm sensitivity. We conducted three field studies, each about 4 weeks: (1) A preliminary study (N=10) to determine reasonable interruptible-moments; (2) A qualitative study (N=20) to assess the design and effectiveness of our extension; and (3) A near-identical study (N= ~3K) to assess quantitatively the effect of the timing parameters. Across all conditions Feature Recommender helped users adopt on average 18% of the features they were recommended, and as many as 24% when they were delivered at random times with a 1-per-day delivery rate limit. We show that lack of trust in recommendations is a key factor in hindering their effectiveness.

Text

2017-01-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/59761

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/