Topics:

00:00 Intro

01:30 Doug’s story in Search

04:55 How Quepid came about

10:57 Relevance as product at Shopify: challenge, process, tools, evaluation

15:36 Search abandonment in Ecommerce

21:30 Rigor in A/B testing

23:53 Turn user intent and content meaning into tokens, not words into tokens

32:11 Use case for vector search in Maps. What about search in other domains?

38:05 Expanding on dense approaches

40:52 Sparse, dense, hybrid anyone?

48:18 Role of HNSW, scalability and new vector databases vs Elasticsearch / Solr dense search

52:12 Doug’s advice to vector database makers

58:19 Learning to Rank: how to start, how to collect data with active learning, what are the ML methods and a mindset

1:12:10 Blending search and recommendation

1:16:08 Search engineer role and key ingredients of managing search projects today

1:20:34 What does a Product Manager do on a Search team?

1:26:50 The magical question of WHY

1:29:08 Doug’s announcements

Show notes:

Doug’s post in Shopify’s blog “Search at Shopify—Range in Data and Engineering is the Future”: https://shopify.engineering/search-at-shopify

Using Bayesian optimization for Elasticsearch relevance: https://www.youtube.com/watch?v=yDcYi-ANJwE&t=1s

Hello LTR: https://github.com/o19s/hello-ltr

Quepid: https://quepid.com/

Podcast design: Saurabh Rai [https://twitter.com/srvbhr]

Doug Turnbull - Staff Relevance Engineer, Shopify - Search as a constant experimentation cycle

Vector Podcast