Arithmetic coding revisited

Over the last decade, arithmetic coding has emerged as an important compression tool. It is now the method of choice for adaptive coding on multisymbol alphabets because of its speed, low storage requirements, and effectiveness of compression. This article describes a new implementation of arithmetic coding that incorporates several improvements over a widely used earlier version by Witten, Neal, and Cleary, which has become a de facto standard. These improvements include fewer multiplicative operations, greatly extended range of alphabet sizes and symbol probabilities, and the use of low-precision arithmetic, permitting implementation by fast shift/add operations. We also describe a modular structure that separates the coding, modeling, and probability estimation components of a compression system. To motivate the improved coder, we consider the needs of a word-based text compression program. We report a range of experimental results using this and other models. Complete source code is available

cwl_eval : An evaluation tool for information retrieval

We present a tool (“cwl_eval”) which unifies many metrics typically used to evaluate information retrieval systems using test collections. In the C/W/L framework metrics are specified via a single function which can be used to derive a number of related measurements: Expected Utility per item, Expected Total Utility, Expected Cost per item, Expected Total Cost, and Expected Depth. The C/W/L framework brings together several independent approaches for measuring the quality of a ranked list, and provides a coherent user model-based framework for developing measures based on utility (gain) and cost.Here we outline the C/W/L measurement framework; describe the cwl_eval architecture; and provide examples of how to use it. We provide implementations of a number of recent metrics, including Time Biased Gain, U-Measure, Bejewelled Measure, and the Information Foraging Based Measure, as well as previous metrics such as Precision, Average Precision, Discounted Cumulative Gain, Rank-Biased Precision, and INST. By providing state-of-the-art and traditional metrics within the same framework, we promote a standardised approach to evaluating search effectiveness

An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility Metric

Many evaluation metrics have been defined to evaluate the effectiveness ad-hoc retrieval and search result diversification systems. However, it is often unclear which evaluation metric should be used to analyze the performance of retrieval systems given a specific task. Axiomatic analysis is an informative mechanism to understand the fundamentals of metrics and their suitability for particular scenarios. In this paper, we define a constraint-based axiomatic framework to study the suitability of existing metrics in search result diversification scenarios. The analysis informed the definition of Rank-Biased Utility (RBU) -- an adaptation of the well-known Rank-Biased Precision metric -- that takes into account redundancy and the user effort associated to the inspection of documents in the ranking. Our experiments over standard diversity evaluation campaigns show that the proposed metric captures quality criteria reflected by different metrics, being suitable in the absence of knowledge about particular features of the scenario under study.Comment: Original version: 10 pages. Preprint of full paper to appear at SIGIR'18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, July 8-12, 2018, Ann Arbor, MI, USA. ACM, New York, NY, US

Building economic models and measures of search

Economics provides an intuitive and natural way to formally represent the costs and benefits of interacting with applications, interfaces and devices. By using economic models it is possible to reason about interaction, make predictions about how changes to the system will affect behavior, and measure the performance of people's interactions with the system. In this tutorial, we first provide an overview of relevant economic theories, before showing how they can be applied to formulate different ranking principles to provide the optimal ranking to users. This is followed by a session showing how economics can be used to model how people interact with search systems, and how to use these models to generate hypotheses about user behavior. The third session focuses on how economics has been used to underpin the measurement of information retrieval systems and applications using the C/W/L framework (which reports the expected utility, expected total utility, expected total cost, and so on) - and how different models of user interaction lead to different metrics. We then show how information foraging theory can be used to measure the performance of an information retrieval system - connecting the theory of how people search with how we measure it. The final session of the day will be spent building economic models and measures of search. Here sample problems will be provided to challenge participants, or participants can bring their own