Multi-Stage Search Architectures for Streaming Documents

Title of dissertation: Multi-Stage Search Architectures for Streaming Documents Nima Asadi, Doctor of Philosophy, 2013 Dissertation directed by: Associate Professor Jimmy Lin Department of Computer Science and College of Information Studies The web is becoming more dynamic due to the increasing engagement and contribution of Internet users in the age of social media. A more dynamic web presents new challenges for web search—an important application of Information Retrieval (IR). A stream of new documents constantly flows into the web at a high rate, adding to the old content. In many cases, documents quickly lose their relevance. In these time-sensitive environments, finding relevant content in response to user queries requires a real-time search service; immediate availability of content for search and a fast ranking, which requires an optimized search architecture. These aspects of today’s web are at odds with how academic IR researchers have traditionally viewed the web, as a collection of static documents. Moreover, search architectures have received little attention in the IR literature. Therefore, academic IR research, for the most part, does not provide a mechanism to efficiently handle a high-velocity stream of documents, nor does it facilitate real-time ranking. This dissertation addresses the aforementioned shortcomings. We present an efficient mechanism to index a stream of documents, thereby enabling immediate availability of content. Our indexer works entirely in main memory and provides a mechanism to control inverted list contiguity, thereby enabling faster retrieval. Additionally, we consider document ranking with a machine-learned model, dubbed “Learning to Rank” (LTR), and introduce a novel multi-stage search architecture that enables fast retrieval and allows for more design flexibility. The stages of our architecture include candidate generation (top k retrieval), feature extraction, and document re-ranking. We compare this architecture with a traditional monolithic architecture where candidate generation and feature extraction occur together. As we lay out our architecture, we present optimizations to each stage to facilitate low-latency ranking. These optimizations include a fast approximate top k retrieval algorithm, document vectors for feature extraction, architectureconscious implementations of tree ensembles for LTR using predication and vectorization, and algorithms to train tree-based LTR models that are fast to evaluate. We also study the efficiencyeffectiveness tradeoffs of these techniques, and empirically evaluate our end-to-end architecture on microblog document collections. We show that our techniques improve efficiency without degrading quality. Multi-Stage Search Architectures for Streaming Documents by Nima Asadi Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2013 Advisory Committee: Associate Professor Jimmy Lin, Chair/Advisor Assistant Professor Hal Daumé III Associate Professor Amol Deshpande Professor Alan Sussman Professor Carol Y. Espy-Wilson c © Copyright by Nima Asadi 2013 Dedication To Katherine, Sina, Christina, and my parents.