Let's talk about Search Relevance

Oct 26, 2021 · București, Romania


starting October we will launch a new type of meeting in our community: Topic based meeting. What does this mean? A subject is proposed by one of our community members and we get together (this will be limited to 20 participants meeting) and discuss/learn/share - the same member of the community taking care of the moderation and organisation of the meeting. We're just starting this in October thus I expect that in time we will fine tune the idea, but until then we propose in October to meet (physically, the location is still TBD - we are looking for an outdoor place) and discuss Search Relevance. See the details below.

October organizer and moderator: Radu Gheorghe, Software Engineer (https://www.linkedin.com/in/ragheorghe/)

Let's discuss how to approach the big&fuzzy problem of search relevance. For example, when people search for "iphone" to stop giving them iphone covers first :)

I can start by sharing some of my experience (with Elasticsearch/Solr):
- lexical matching to find the right words, tolerating typos, stemming, etc
- statistical-based relevance like TF/IDF and BM25
- combining matches from different fields (e.g. product title, description...) and boosting
- injecting external info such as sales, reviews, clicks into the relevance score
- exploration via autocomplete, did you mean, faceting

And I can also scratch the surface of:
- algorithmic boost tuning, so that we stop guessing boosts
- query classification, entity extraction to make the query more targeted
- semantic/vectorized search, to hopefully stop returning chocolate-flavored X when people search for "chocolate"
- learning to rank -> using ML to inject said sales, reviews or click info in a more flexible way

I'm sure that each E-commerce implements some of the above (or some that I've missed) in different combinations. Let's share ideas of what we tried, what worked and what we think of trying :)

Radu & Valentina

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