How Flipkart made their type ahead search hyper personalized
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In the video, I discussed how Flipkart personalized its type-ahead search suggestions, focusing on high-level architecture and key design decisions. To enhance user experience, personalized suggestions based on user history were prioritized. Factors such as suggestion quality, performance, and grammar were considered for ranking. User intent was analyzed using product taxonomy, past searches, and browsing history. Personalization was achieved by training a model using user interactions. The system utilized XGBoost with LTR ranking in a search engine like Solar for efficient suggestion delivery.
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Learning system design from such a experienced person is something I recommend and not from just a college passout and somehow managed to get into Google and FB and started teaching system design. As this only gets better with experience. No shortcut!
Great video Arpit. Your knowledge never ceases to leave me in awe
I have just recently work on hibernate search apache lucene for indexing & searching the query results. In this, we used fuzzy & wildcard query to search in an lucene index. Your videos help us to clear the basics of any tech stack.👍
my understanding of type ahead was so limited, thanks a lot arpit.
Never thought typeahead were this advanced
Awesome video bhaiya💗
thanks for this
Thanks a ton for this Arpit, would like to know more about how taxonomy /RDF being used in real search systems. Also, apart from Solr in LTR mode, do we really need graph DB like neo4j for enabling semantic search. and answering complex search
heavy content bruh!
Any idea how they provide the results for an user that didn't login? Cache layer is one obvious answer. location may be another parameter for suggestion. IP address?!!!
Can you please share the Reformulating search Model building tutorial. That’s fascinating 😮
@AsliEngineering
Жыл бұрын
I prototyped it on my local but not sure if they do similar.