H
Honghua Dai
Researcher at Deakin University
Publications - 95
Citations - 3123
Honghua Dai is an academic researcher from Deakin University. The author has contributed to research in topics: Causal model & Knowledge extraction. The author has an hindex of 19, co-authored 94 publications receiving 3014 citations. Previous affiliations of Honghua Dai include DePaul University & Monash University, Clayton campus.
Papers
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Proceedings ArticleDOI
Effective personalization based on association rule discovery from web usage data
TL;DR: This paper proposes effective and scalable techniques for Web personalization based on association rule discovery from usage data that can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.
Journal ArticleDOI
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
TL;DR: The results indicate that using the generated aggregate profiles, the technique can achieve effective personalization at early stages of users' visits to a site, based only on anonymous clickstream data and without the benefit of explicit input by these users or deeper knowledge about them.
Proceedings ArticleDOI
Learning from facial aging patterns for automatic age estimation
TL;DR: The AGES (AGing pattErn Subspace) method for automatic age estimation is proposed, which aims to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace.
Book ChapterDOI
Integrating Web Usage and Content Mining for More Effective Personalization
TL;DR: This paper presents a framework for Web usage mining, distinguishing between the offine tasks of data preparation and mining, and the online process of customizing Web pages based on a user's active session, and describes effective techniques based on clustering to obtain a uniform representation for both site usage and site content profiles.
Proceedings ArticleDOI
Using sequential and non-sequential patterns in predictive Web usage mining tasks
TL;DR: An efficient framework for Web personalization based on sequential and non-sequential pattern discovery from usage data is described, which indicates that more restrictive patterns are more suitable for predictive tasks, such as Web prefetching, while less constrained patterns are less effective alternatives in the context of Webpersonalization and recommender systems.