A universal topic framework (UniZ) and its application in online search

Probabilistic topic models, such as PLSA and LDA, are gaining popularity in many fields due to their high-quality results. Unfortunately, existing topic models suffer from two drawbacks: (1) model complexity and (2) disjoint topic groups. That is, when a topic model involves multiple entities (such as authors, papers, conferences, and institutions) and they are connected through multiple relationships, the model becomes too difficult to analyze and often leads to intractable solutions. Also, different entity types are classified into disjoint topic groups that are not directly comparable, so it is difficult to see whether heterogeneous entities (such as authors and conferences) are on the same topic or not (e.g., are Rakesh Agrawal and KDD related to the same topic?). In this paper, we propose a novel universal topic framework (UniZ) that addresses these two drawbacks using“prior topic incorporation.” Since our framework enables representation of heterogeneous entities in a si...

• Type: Journal
• Year: 2015
• Where: SAC
• Authors: Youngchul Cha, Keng-hao Chang, Hari Bommaganti, Ye Chen, Tak W. Yan, Bin Bi, Junghoo Cho
• Keywords: Applied Computing, SAC 2015