(Founder of Neeva here) Query-independent page and site signals like PageRank (or its site variants) have limited utility in a search ranker. Mostly, they are

(Founder of Neeva here) Query-independent page and site signals like PageRank (or its site variants) have limited utility in a search ranker. Mostly, they are useful in weeding out bad pages and sites from your index, tie-breaking when you hit shard limits in retrieval and a few other edge cases. The signals that matter the most: 1. Anchor text (and all variants of smearing and distinguishing between high quality and low quality anchors) 2. In aggregate, which pages got clicked on on any given query (and all smearing variants -- using ngrams, embeddings, ...). At Neeva, we use it for retrieval and scoring. 3. Query understanding signals mined from the query-click bi-partite graph and the query-query session refinement graph. 4. Page summarization signals built on top of 1 and 2 and body text. 5. To a lesser extent, query-independent page quality signals Whether you use term-based retrieval or nearest neighbor (embedding) retrieval, a heuristic combination of signals or LambdaMart, whether you calibrate to human eval or clicks, whether your topical relevance function is hand crafted or uses a combination of deep learning and IR signals are all details past that. tldr; there's a lot of craft in a search ranker, and no one silver bullet. Definitely not just PageRank.