From Text to Context: How We Introduced a Modern Hybrid Search
- Track:
- PyData: Deep Learning, NLP, CV
- Type:
- Talk (long session)
- Level:
- intermediate
- Room:
- North Hall
- Start:
- 10:45 on 10 July 2024
- Duration:
- 45 minutes
Abstract
Customers only buy the products they are able to find. Improving the search functions on the website is crucial for user-friendliness.
In our talk we present the lessons learnt from improving the search of our global online marketplace, which sells 20 million products per year. We moved from a traditional word-match based approach (BM25) to a modern hybrid solution that combines BM25 with a semantic vector model, an open-source language model that we fine-tuned to our domain.
With numerous references to current literature, we will explain how we designed our new system and solved the multiple challenges we encountered on both the ML and engineering side (data pipeline encoding documents, live service encoding queries, integration with search engine). Our system is based on OpenSearch, the lessons can be applied to other search engines as well.
In particular the presentation will cover:
- Status and Short-Comings of our old Search
- Introduction of Hybrid Search
- Our Machine Learning Solution
- Architecture and Implementation (with special consideration of latency)
- Learnings and Next Steps