Advances in information technology prompt a tremendous usage growth of the Internet. Online activities, such as e-commerce, social interaction, etc., have drawn increasing attentions in regard to the provision of personalized services which require best and comprehensive understanding of users. As an approach, this study outlines a general framework based on human (or consumer) contexts for the discovery and creation of business intelligence. Three major portions are discussed. First, the collection of human contexts, including activity logs in both cyber and physical worlds, is modeled. Second, data analysis was performed via proposed mining algorithms that concern potential fusion at different levels according to situations and ultimate purposes. Third, sustenance of developed model is then concentrated. An open platform was developed to support the evolutionary process of human models, and to allow contributions (e.g., data sharing, accessing, etc.) from third parties.
Provision of active and personalized services relies on the understanding of individual behaviour. In this study, a broad spectrum of online purchasing scenarios was analyzed in order to define a general model termed the Consumer Behaviour Model (CBM). Contexts, including purchase activities, environmental data, etc., so-called big data, are important sources for extracting the characteristics of consumers, contributing to the growth of CBM. This research proposes a framework which includes a data mining engine and a knowledge fusion engine for the continuous extraction of customer purchasing behaviour.Moreover, an open platform has been designed for facilitating access to CBM by third-party applications. In this paper, two consumer purchasing scenarios are posited along with an illustration of how the Consumer Behaviour Model grows accordingly.Keywords-consumer behaviour model; smart business service; personalization; adaptive advertising I.
Families are the essential building block of every society and making each family happy is the key to a happy society. Our research gives an outline of a smart home system for creating a harmonious family living environment. The system has to have three elements: acquisition of data on individual activities and their surroundings; data mining for activity recognition; and a personalized recommendation engine. This paper briefly describes the simulated sensors and home environment, introduces our novel approach of the matrix-based data mining and emphasizes a personalized recommendation engine which aims to prevent negative phenomena from taking place. Two particular scenarios, in the context of child education and care of the elderly, are provided to show how the recommendation engine functions.Index Terms-happy home system, individual personalized model, recommendation engine, activity matrix, data mining
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