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.

 

Video

Kesselhaus
15.06.2021 21:20 – 22:20
Lightning Talk
Intermediate

Speakers