- This paper represents a new context-aware learning system to provide services in ubiquitous computing environment. The aim is to precisely decide which services each user provides. To achieve this goal, we design a preprocessing method (i.e., context modeling) to obtain good information which represents user's characteristics from context-aware information (i.e., user profiles) which consists of states: who, when, where, why, what, and how: 5W1H. The proposed system applies the state-of-the-art naïve Bayesian Decision Theory, which is one of the statistical analyses based on probability theorem.
Index Terms — context-aware application, naïve Bayesian classifier, and user’s preference learner
Rbeen developing to provide various services to each user in ECENTLY, many context-aware applications (CAAs) have ubiquitous computing environment. CAAs help each user to decide his or her decision as extracting meaningful information from many kinds of contexts such as locations, identifications, activities, states of users, and so on. These services apply most of the real world entertainments such as health, education, accident, rescue, and shopping.
To provide more efficient services, most of the CAAs have tried to employ machine learning (ML) techniques with learning data. From this integration of context-aware and ML technologies, we can configure artificial intelligent service systems.
In this paper, we propose a new context-aware learning
system using a naïve Bayesian classifier which is one of the
most popular ML methods. The main goal of the proposed
system automatically recommends suitable services to each
user. To learn this system, we employ 6 kinds of contexts (i.e.,
user profile: who, when, where, why, what, and how (5W1H))
[
The paper is organized as follows. In section 2, we introduce the proposed context-aware learning system. The paper concludes in section 3.
This research is the sub-system of user profile management
system which plays a role in managing all data for
personalizing users [
First, the context modeling processes context-information
from data collected by many kinds of sensors, time, and user
preference [
Table. 1 Data types and values
The second part is preference learner for users. This
process consists of a rule-based engine and a naïve Bayesian
classifier. The rule-based engine generates (Rule Constructor),
saves, and manages rules (Experience) [
However, there are 3 constraints in the proposed
context-aware learning system. Firstly, we should control the
number of training data due to limitation of resources. Next, we
should prevent needless update because of efficient
computation time. In the above constraint, we divide 3 learning
modes [
In this paper, we have introduced a context-aware learning
system to recommend services into each user in ubiquitous
environment. The proposed system is divided by two parts: the
context modeling process and the naïve Bayesian classification
process to predict services for extracting high level information
from low level data. In the future, we will apply various
statistical machine learning methods such as advanced naïve
Bayesian methods (e.g., selective Bayesian classifier, a
tree-augmented naïve Bayesian classifier (TAN), etc) and
Bayesian neural networks to improve the performance for
providing highly qualitative services [
This research is supported by the UCN Project, the MCI 21st Century Frontier R&D Program in Korea