In the vast expanse of the Internet, Google’s indexing process acts as a careful librarian, meticulously cataloging web content. This indexing, likened to an index at the end of a book, is the foundation of Google’s search engine capabilities.
Google’s ambitions go beyond size; aspires to create a “comprehensive index”. While the size of the index in 2020 was estimated to be around 400 billion documents, the emphasis is not on volume alone, but on quality. “Bigger is not necessarily better” because reducing redundancy and removing irrelevant information makes for a more valuable index.
Understanding the role of the index in information retrieval reveals the complexity beneath the surface. When a query appears, Google crawls the index and uses complex algorithms to retrieve relevant documents. This includes traversing post lists, cross-referencing information, and using signals like page rank to prioritize high-quality pages.
Google’s evaluation process further refines the retrieved documents. The challenge lies in the amount of documents that could match the query. To solve this problem, Google uses a diverse set of algorithms and machine learning models that use more than a hundred signals. Basic signals include document content, timeliness, page quality, reliability, localization and Navboost.
Navboost, a system rooted in user interactions, plays a key role. Trained on clicks over the past 13 months, Navboost slices and dices the data to create different sets for mobile and desktop searches. Glue, another integral signal, includes everything on a search engine results page (SERP) that is not a website result.
Deep learning enters the scene in 2015 with RankBrain, DeepRank, RankEmbed BERT and the MUM monster. These models adjust document scores, rely on language understanding, world knowledge, and training on massive datasets. However, Google remains cautious about putting full trust in deep learning for rankings, stressing the need for scrutiny.
Tangram, formerly Tetris, adds search features that cannot be obtained over the web. Google ranks SERPs using an IS (Information Satisfaction) score, a human-centric metric derived from search quality ratings.
Human reviewers play a vital role in improving search quality, but problems arise. Reviewers may struggle with technical questions, misjudge popularity, or overlook freshness. Google’s continuous ranking methodology, which uses trillions of click examples, aims to mimic the behavior of a human searcher.
As Google evolves, understanding its complex algorithms, machine learning systems, and the fine balance between user feedback and algorithmic decision-making becomes essential. A journey through Google’s search environment reveals a complex web of algorithms, signals and ratings that are constantly being fine-tuned to provide users with the most relevant and satisfying searches possible.