이종욱 교수

Skyline queries have gained a lot of attention for multi-criteria analysis in large-scale datasets. While existing skyline algorithms have focused mostly on exploiting data dominance to achieve efficiency, we propose that data incomparability should be treated as another key factor in optimizing skyline computation. Specifically, to optimize both factors, we first identify common modules shared by existing non-index skyline algorithms, and then analyze them to develop a cost model to guide a bal

We develop a novel framework, named as <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula> -injection, to address the sparsity problem of recommender systems. By carefully injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top- <i>N</i> recommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion of <i>pre-use preferences</i> o

We demonstrate Product EntityCube, a product recommendation and navigation system. While the unprecedented scale of a product search portal enables to satisfy users with diverse needs, this scale also complicates product recommendation. Specifically, our target application poses a unique challenge of overcoming insufficient user profiles and feedbacks. To address this problem, we organize query results into clusters representing different user perceptions of similarity, and provide a navigationa

Skyline queries have gained considerable attention for multi-criteria analysis of large-scale datasets. However, the skyline queries are known to return too many results for high-dimensional data. To address this problem, a skycube is introduced to efficiently provide users with multiple skylines with different strengths. For efficient skycube construction, state-of-the-art algorithms amortized redundant computation among subspace skylines , or cuboids , either (1) in a bottom-up fashion with th