Abstract
Soft keyboard has been popular since the smartphone made an appearance. As the smartphone is a personal device used for user's preference, we need to personalize the soft keyboard. In order to make a personal context-aware keyboard, a large amount of data is required from each individual, but it is difficult to collect the data enough. In this paper, we propose a novel method to construct random forests with additional data of the same clusters, which copes with small amount of the individual data by adding similar data through K-means clustering algorithm. In addition, for the personalization of the soft keyboard, the preferred GUIs are recommended according to the activities and input hand postures of the user recognized by the random forest models trained with the clustered data. To train the proposed system, we have collected the data from 200 people. Each person can use the most necessary keyboard by selecting the appropriate GUI for each situation depending on the smartphone usage activity and input hand postures. The proposed system showed better performance than a model using all common data and a model using individual data only.
| Original language | English |
|---|---|
| Pages (from-to) | 17-27 |
| Number of pages | 11 |
| Journal | Neurocomputing |
| Volume | 353 |
| DOIs | |
| Publication status | Published - 2019 Aug 11 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
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A personalized context-aware soft keyboard adapted by random forest trained with additional data of same cluster. / Jo, Sang Muk; Cho, Sung-Bae.
In: Neurocomputing, Vol. 353, 11.08.2019, p. 17-27.Research output: Contribution to journal › Article
TY - JOUR
T1 - A personalized context-aware soft keyboard adapted by random forest trained with additional data of same cluster
AU - Jo, Sang Muk
AU - Cho, Sung-Bae
PY - 2019/8/11
Y1 - 2019/8/11
N2 - Soft keyboard has been popular since the smartphone made an appearance. As the smartphone is a personal device used for user's preference, we need to personalize the soft keyboard. In order to make a personal context-aware keyboard, a large amount of data is required from each individual, but it is difficult to collect the data enough. In this paper, we propose a novel method to construct random forests with additional data of the same clusters, which copes with small amount of the individual data by adding similar data through K-means clustering algorithm. In addition, for the personalization of the soft keyboard, the preferred GUIs are recommended according to the activities and input hand postures of the user recognized by the random forest models trained with the clustered data. To train the proposed system, we have collected the data from 200 people. Each person can use the most necessary keyboard by selecting the appropriate GUI for each situation depending on the smartphone usage activity and input hand postures. The proposed system showed better performance than a model using all common data and a model using individual data only.
AB - Soft keyboard has been popular since the smartphone made an appearance. As the smartphone is a personal device used for user's preference, we need to personalize the soft keyboard. In order to make a personal context-aware keyboard, a large amount of data is required from each individual, but it is difficult to collect the data enough. In this paper, we propose a novel method to construct random forests with additional data of the same clusters, which copes with small amount of the individual data by adding similar data through K-means clustering algorithm. In addition, for the personalization of the soft keyboard, the preferred GUIs are recommended according to the activities and input hand postures of the user recognized by the random forest models trained with the clustered data. To train the proposed system, we have collected the data from 200 people. Each person can use the most necessary keyboard by selecting the appropriate GUI for each situation depending on the smartphone usage activity and input hand postures. The proposed system showed better performance than a model using all common data and a model using individual data only.
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U2 - 10.1016/j.neucom.2018.05.128
DO - 10.1016/j.neucom.2018.05.128
M3 - Article
VL - 353
SP - 17
EP - 27
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -