892 citations · 2.3k across the 60 of their papers we have counts for
6 papers · 1 filter
Discovering topic structures of a temporally evolving document corpus
Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung +1
In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimens…
Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature
Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung +1
In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimens…
Preference Networks: Probabilistic Models for Recommendation Systems
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preferen…
Learning Rank Functionals: An Empirical Study
Truyen Tran, Dinh Phung, Svetha Venkatesh
Ranking is a key aspect of many applications, such as information retrieval, question answering, ad placement and recommender systems. Learning to rank has the goal of estimating a…
Ordinal Boltzmann Machines for Collaborative Filtering
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh
Collaborative filtering is an effective recommendation technique wherein the preference of an individual can potentially be predicted based on preferences of other members. Early a…
Probabilistic Models over Ordered Partitions with Application in Learning to Rank
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh
This paper addresses the general problem of modelling and learning rank data with ties. We propose a probabilistic generative model, that models the process as permutations over pa…