Learning from our Multi-Stage Collaborative Autoethnography

Learning from our elders.

Multi-Stage Multi-Task Feature Learning

Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to...

Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction

We investigate the problem of personalized reviewbased rating prediction which aims at predicting users’ ratings for items that they have not evaluated by using their historical reviews and ratings. Most of existing methods solve this problem by integrating topic model and latent factor model to learn interpretable user and items factors. However, these methods cannot utilize word local context...

A Multi-stage Collaborative 3D GIS to Support Public Participation

This paper presents a collaborative 3D GIS to support public participation. Realizing that public-involved decision making is often a multi-stage process, the proposed system is designed to provide coherent support for collaborations in the different stages. We differentiate ubiquitous participation and intensive participation, and identify their suitable application stages. The proposed system...

Doing autoethnography

The author has argued elsewhere that individual identity is sufficiently worthy of research and more than just a deviant case. The representation of an individual’s story that contains one of society’s taboos appears to require legitimation of not only the text but also the method by which it is conveyed. This is particularly important if memory and its distortions appear to be critical feature...