TY - GEN
T1 - Functionality-based clustering using short textual description
T2 - 18th International Conference on Intelligent User Interfaces, IUI 2013
AU - Lulu, David Lavid Ben
AU - Kuflik, Tsvi
PY - 2013/3/19
Y1 - 2013/3/19
N2 - In recent years, we have witnessed the incredible popularity and widespread adoption of mobile devices. Millions of Apps are being developed and downloaded by users at an amazing rate. These are multi-feature Apps that address a broad range of needs and functions. Nowadays, every user has dozens of Apps on his mobile device. As time goes on, it becomes more and more difficult simply to find the desired App among those that are installed on the mobile device. In spite of several attempts to address the problem, no good solution for this increasing problem has yet been found. In this paper we suggest the use of unsupervised machine learning for clustering Apps based on their functionality, to allow users to access them easily. The functionality is elicited from their description as retrieved from various App stores and enriched by content from professional blogs. The Apps are clustered and grouped according to their functionality and presented hierarchically to the user in order to facilitate the search on the small screen of the mobile device.
AB - In recent years, we have witnessed the incredible popularity and widespread adoption of mobile devices. Millions of Apps are being developed and downloaded by users at an amazing rate. These are multi-feature Apps that address a broad range of needs and functions. Nowadays, every user has dozens of Apps on his mobile device. As time goes on, it becomes more and more difficult simply to find the desired App among those that are installed on the mobile device. In spite of several attempts to address the problem, no good solution for this increasing problem has yet been found. In this paper we suggest the use of unsupervised machine learning for clustering Apps based on their functionality, to allow users to access them easily. The functionality is elicited from their description as retrieved from various App stores and enriched by content from professional blogs. The Apps are clustered and grouped according to their functionality and presented hierarchically to the user in order to facilitate the search on the small screen of the mobile device.
KW - Clustering
KW - Data mining
KW - Human-computer interaction
KW - Mobile
KW - Short and sparse text
KW - Smartphone apps
KW - Text similarity
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=84875824890&partnerID=8YFLogxK
U2 - 10.1145/2449396.2449434
DO - 10.1145/2449396.2449434
M3 - Conference contribution
AN - SCOPUS:84875824890
SN - 9781450320559
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 297
EP - 305
BT - IUI 2013 - Proceedings of the 18th International Conference on Intelligent User Interfaces
Y2 - 19 March 2013 through 22 March 2013
ER -