Stan Chan

Paul Anderson photo

Sr Software Engineer

Stan brings to the session a strong foundation of more than 20+ years of technology experience in Silicon Valley. During this time, he has put on many hats, including systems engineering, application development, product operations, product management, release engineering and consulting roles in many industries including retail, financial, energy, travel and software companies of varying sizes from large enterprises to small startups. He enjoys going deep into technology around topics that are currently driving the industry, including DevOps, Cloud, Internet of Things, Machine Learning, Serverless, and Blockchain.

Stan Chan is speaking at the following session/s

Supporting Query Tagging/Suggestion in Fusion 4.2

Wednesday | 3:20PM - 4:00PM | Columbia 8

The basic text processing steps include stemming, removing stop words, expanding words via lemmatization, implementing synonyms, and tokenization. These have all been used in the search. However, these processes do not provide insights into the user query. Matching query terms with patterns captured from user’s activities is a useful strategy for query tagging – the idea is to give users access to relevant queries in the corpus that match their interest. Several ML and NLP mechanisms exist at Uber to facilitate query tagging in different domains. We collaborate between teams to integrate the existing tagging architecture with the Fusion 4.2 platform. After integrating the existing models into Fusion we evaluate auto-suggest and query completion since these usually require careful consideration to do well.

Audience Takeaway
If you thought of implementing ML models with Fusion, but always wondered how to start or what is involved in the process. We will cover the entire journey of integration between Fusion and another ML/NLP architecture to fulfill the specific requirements of query classification and suggestion.

Intended Audience
Attendees should be familiar with Fusion concepts. A basic understanding of machine learning or natural language processing is recommended.


Additional speakers