A New Probabilistic Model for Top-k Ranking Problem

ABSTRACT This paper is concerned with top-k ranking problem, which reflects the fact that people pay more attention to the top ranked objects in real ranking application like information retrieval. A popular approach to top-k ranking problem is based on probabilistic models, such as Luce model and Mallows model. However, whether the sequential generative process described in these models is a suitable way for top-k ranking remains a question. According to the riffled independence factorization proposed in recent literature, which is a natural structural assumption on top-k ranking, we propose a new generative process of top-k ranking data. Our approach decomposes distributions over the top-k ranking into two layers: the first layer describes the relative ordering between the top k objects and the rest n − k objects, and the second layer describes the full ordering on the top k objects. On this basis, we propose a new probabilistic model for top-k ranking problem, called hierarchical ordering model. Specifically, we use three different probabilistic models to describe different generative processes of the first layer, and Luce model to describe the sequential generative process of the second layer, thus we obtain three different specific hierarchical ordering models. We also conduct extensive experiments on benchmark datasets to show that our proposed models can outperform previous models significantly

Acceleration of ListNet for ranking using reconfigurable architecture

Document ranking is used to order query results by relevance with ranking models. ListNet is a well-known ranking approach for constructing and training learning-to-rank models. Compared with traditional learning approaches, ListNet delivers better accuracy, but is computationally too expensive to learn models with large data sets due to the large number of permutations and documents involved in computing the gradients. Currently, the long training time limits the practicality of ListNet in ranking applications such as breaking news search and stock prediction, and this situation is getting worse with the increase in data-set size. In order to tackle the challenge of long training time, this thesis optimises the ListNet algorithm, and designs hardware accelerators for learning the ListNet algorithm using Field Programmable Gate Arrays (FPGAs), making the algorithm more practical for real-world application. The contributions of this thesis include: 1) A novel computation method of the ListNet algorithm for ranking. The proposed computation method exposes more fine-grained parallelism for FPGA implementation. 2) A weighted sampling method that takes into account the ranking positions, along with an effective quantisation method based on FPGA devices. The proposed design achieves a 4.42x improvement over GPU implementation speed, while still guaranteeing the accuracy. 3) A full reconfigurable architecture for the ListNet training using multiple bitstream kernels. The proposed method achieves a higher model accuracy than pure fixed point training, and a better throughput than pure floating point training. This thesis has resulted in the acceleration of the ListNet algorithm for ranking using FPGAs by applying the above techniques. Significant improvements in speed have been achieved in this work against CPU and GPU implementations.Open Acces

Practice based competency development: a study of resource geologists and the JORC code system

The mining industry is a major contributor to the Australian economy. The value of mining and exploration shares traded on the Australian Stock Exchange are contingent on the estimates of mineral deposits, which are disclosed publically in accordance with a reporting code maintained by the Australasian Joint Ore Reserves Committee (the JORC Code). Expert resource geologists, known as Competent Persons, provide classified estimates of mineral endowment that underpin these public statements. The JORC Code requirements for qualifying as Competent Persons are membership of an approved professional association and a minimum of five years’ relevant experience. This research set out to address a primarily practical issue: How do the mining industry, mining companies and individuals cooperate to develop resource geologists with sufficient competency to provide expert recommendations for public reporting of mineral resources? A corollary to this is ‘Are the current standards sufficient to identify the competency expectations placed on Competent Persons?’ It is challenging to place the subsequent research in any one discipline as the study draws on multiple theories across multiple domains to facilitate a relevant description of the practicebased competency development. To properly understand the the practice of resource geologists operating in a sub-sector within the JORC Code system, the research needed to explore and consolidate diverse theories such as theories on social structures, workplace learning theories and statistical reasoning education theories. In addition, as a mixed methods study, the research draws on a wide range of tools from qualitative iterative coding and theming techniques to the more rigorous statistical tools of t-tests, paired t-tests, ANOVA and the philosophically different Rasch Analysis method. This study reflects a broad curiosity in diverse concepts and theories that is combined with the researcher’s desire to provide a meaningful practical contribution to the mining industry. The practical outcome of this research is a revised set of criteria to meet Competent Persons status under the JORC Code that is supported by a competency development model. These models are generalised to reflect a revised competency model, based on the dual expectations of practice exposure and reasoning ability, and an associated competency development model, which synthesises contributions of workplace learning experiences. The contributions to the theory include a revised theory of workplace learning networks emerging from the practice context of transient professional workers. These networks are enduring, transient and egocentric and operate beyond organisational confines