Feature Recommender : a large-scale in-situ study of proactive software feature recommendations
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...
Main Author: | Ardekani, Kamyar |
---|---|
Language: | English |
Published: |
University of British Columbia
2016
|
Online Access: | http://hdl.handle.net/2429/59761 |
Similar Items
-
QFRecs - Recommending Features in Feature-Rich Software based on Web Documentation
by: Khan, Md Adnan Alam
Published: (2015) -
Apply Feature Extraction, SVM and LSA to Analyze Large-Scale Data for Recommendation Systems
by: John C. Chien, et al.
Published: (2008) -
Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets
by: S. Bhaskaran, et al.
Published: (2020-07-01) -
A Proactive Recommendation System for Writing in the Internet Age
by: Olga Muñoz, et al.
Published: (2010-03-01) -
A hybrid model for context-aware proactive recommendation
by: Akermi, Imen
Published: (2017)