Declarative Experimentation in Information Retrieval Using PyTerrier

The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed more expressive representations of deep neural network architectures. We argue that such a powerful formalism is missing in information retrieval (IR), and propose a framework called PyTerrier that allows advanced retrieval pipelines to be expressed, and evaluated, in a declarative manner close to their conceptual design. Like the aforementioned frameworks that compile deep learning experiments into primitive GPU operations, our framework targets IR platforms as backends in order to execute and evaluate retrieval pipelines. Further, we can automatically optimise the retrieval pipelines to increase their efficiency to suite a particular IR platform backend. Our experiments, conducted on TREC Robust and ClueWeb09 test collections, demonstrate the efficiency benefits of these optimisations for retrieval pipelines involving both the Anserini and Terrier IR platforms

Efficient query processing for scalable web search

Search engines are exceptionally important tools for accessing information in today’s world. In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search engine are two goals that form a natural trade-off, as techniques that improve the effectiveness of the search engine can also make it less efficient. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for the development of efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing strategies, while also providing the latest trends in the literature in efficient query processing, including the coherent and systematic reviews of techniques such as dynamic pruning and impact-sorted posting lists as well as their variants and optimisations. Our explanations of query processing strategies, for instance the WAND and BMW dynamic pruning algorithms, are presented with illustrative figures showing how the processing state changes as the algorithms progress. Moreover, acknowledging the recent trends in applying a cascading infrastructure within search systems, this survey describes techniques for efficiently integrating effective learned models, such as those obtained from learning-to-rank techniques. The survey also covers the selective application of query processing techniques, often achieved by predicting the response times of the search engine (known as query efficiency prediction), and making per-query tradeoffs between efficiency and effectiveness to ensure that the required retrieval speed targets can be met. Finally, the survey concludes with a summary of open directions in efficient search infrastructures, namely the use of signatures, real-time, energy-efficient and modern hardware and software architectures

Managing tail latency in large scale information retrieval systems

As both the availability of internet access and the prominence of smart devices continue to increase, data is being generated at a rate faster than ever before. This massive increase in data production comes with many challenges, including efficiency concerns for the storage and retrieval of such large-scale data. However, users have grown to expect the sub-second response times that are common in most modern search engines, creating a problem - how can such large amounts of data continue to be served efficiently enough to satisfy end users? This dissertation investigates several issues regarding tail latency in large-scale information retrieval systems. Tail latency corresponds to the high percentile latency that is observed from a system - in the case of search, this latency typically corresponds to how long it takes for a query to be processed. In particular, keeping tail latency as low as possible translates to a good experience for all users, as tail latency is directly related to the worst-case latency and hence, the worst possible user experience. The key idea in targeting tail latency is to move from questions such as "what is the median latency of our search engine?" to questions which more accurately capture user experience such as "how many queries take more than 200ms to return answers?" or "what is the worst case latency that a user may be subject to, and how often might it occur?" While various strategies exist for efficiently processing queries over large textual corpora, prior research has focused almost entirely on improvements to the average processing time or cost of search systems. As a first contribution, we examine some state-of-the-art retrieval algorithms for two popular index organizations, and discuss the trade-offs between them, paying special attention to the notion of tail latency. This research uncovers a number of observations that are subsequently leveraged for improved search efficiency and effectiveness. We then propose and solve a new problem, which involves processing a number of related queries together, known as multi-queries, to yield higher quality search results. We experiment with a number of algorithmic approaches to efficiently process these multi-queries, and report on the cost, efficiency, and effectiveness trade-offs present with each. Ultimately, we find that some solutions yield a low tail latency, and are hence suitable for use in real-time search environments. Finally, we examine how predictive models can be used to improve the tail latency and end-to-end cost of a commonly used multi-stage retrieval architecture without impacting result effectiveness. By combining ideas from numerous areas of information retrieval, we propose a prediction framework which can be used for training and evaluating several efficiency/effectiveness trade-off parameters, resulting in improved trade-offs between cost, result quality, and tail latency

Efficient and effective retrieval using Higher-Order proximity models

Information Retrieval systems are widely used to retrieve documents that are relevant to a user's information need. Systems leveraging proximity heuristics to estimate the relevance of a document have shown to be effective. However, the computational cost of proximity-based models is rarely considered, which is an important concern over large-scale document collections. The large-scale collections also make collection-based evaluation challenging since only a small number of documents are judged given the limited budget. Effectiveness, efficiency and reliable evaluation are coherent components that should be considered when developing a good retrieval system.This thesis makes several contributions from the three aspects. Many proximity-based retrieval models are effective, but it is also important to find efficient solutions to extract proximity features, especially for models using higher-order proximity statistics. We therefore propose a one-pass algorithm based on the PlaneSweep approach. We demonstrate that the new one-pass algorithm reduces the cost of capturing a full dependency relation of a query, regardless of the input representations. Although our proposed methods can capture higher-ordered proximity features efficiently, the trade-offs between effectiveness and efficiency when using proximity-based models remains largely unexplored. We consider different variants of proximity statistics and demonstrate that using local proximity statistics can achieve an improved trade-off between effectiveness and efficiency. Another important aspect in IR is reliable system comparisons. We conduct a series of experiments that explore the interaction between pooling and evaluation depth, interactions between evaluation metrics and evaluation depth and also correlations between two different evaluation metrics. We show that different evaluation configurations on large test collections, where only a limited number of relevance labels are available, can lead to different system comparison conclusions. We also demonstrate the pitfalls of choosing an arbitrary evaluation depth regardless of the metrics employed and the pooling depth of the test collections. Lastly, we provide suggestions on the evaluation configurations for the reliable comparisons of retrieval systems on large test collections. On these large test collections, a shallow judgment pool may be employed as assumed budgets are often limited, which may lead to an imprecise evaluation of system performance, especially when a deep evaluation metric is used. We propose an estimation framework for estimating deep metric score on shallow judgment pools. With an initial shallow judgment pool, rank-level estimators are designed to estimate the effectiveness gain at each ranking. Based on the rank-level estimations, we propose an optimization framework to obtain a more precise score estimate