It is proven that for relatively well-structured data, like in e-commerce for example, a hand tailored search configuration can easily outperform machine learning approaches for relevance. The search configuration considers the different searchable fields, a business taxonomy and ontology, some domain related synonyms, a few specific landing pages, boosts and some business numerical criteria.
In the same way, we describe an approach for relevance in the case of large-scale search engines which is not based on classical "PageRank" and machine learning approaches. We propose a model based on social interactions between communities and individuals that are using or configuring the search engine. We then compare this model with machine learning powered approaches.