Bøger af Sebastian Bruch

This book presents the fundamentals of vector retrieval. To this end, it delves into important data structures and algorithms that have been successfully used to solve the vector retrieval problem efficiently and effectively. This monograph is divided into four parts. The first part introduces the problem of vector retrieval and formalizes the concepts involved. The second part delves into retrieval algorithms that help solve the vector retrieval problem efficiently and effectively. It includes a chapter each on brand-and-bound algorithms, locality sensitive hashing, graph algorithms, clustering, and sampling. Part three is devoted to vector compression and comprises chapters on quantization and sketching. Finally, the fourth part presents a review of background material in a series of appendices, summarizing relevant concepts from probability, concentration inequalities, and linear algebra. The book emphasizes the theoretical aspects of algorithms and presents related theorems and proofs. It is thus mainly written for researchers and graduate students in theoretical computer science and database and information systems who want to learn about the theoretical foundations of vector retrieval.

Information retrieval researchers develop algorithmic solutions to hard problems and insist on a proper, multifaceted evaluation of ideas. As we move towards even more complex deep learning models in a wide range of applications, questions on efficiency once again resurface with renewed urgency. Efficiency is no longer limited to time and space but has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment. This monograph takes a step towards promoting the study of efficiency in the era of neural information retrieval by offering a comprehensive survey of the literature on efficiency and effectiveness in ranking and retrieval. It is inspired by the parallels that exist between the challenges in neural network-based ranking solutions and their predecessors, decision forest-based learning-to-rank models, and the connections between the solutions the literature to date has to offer. By understanding the fundamentals underpinning these algorithmic and data structure solutions one can better identify future directions and more efficiently determine the merits of ideas.