Ranking in Query Auto Completion Systems

This was a part of our tutorial on "Deep Learning for AutoSuggest" tutorial at IJCAI 2022. Part 1: https://youtu.be/eyJSqrxwwGU Part 2: https://youtu.be/7fGnZaifLoE Part 3: https://youtu.be/dO_AJ2Wn0Z0 Part 4: https://youtu.be/nboTXqZTG_I Part 5: https://youtu.be/bNpiTFzJkxs Query Auto Completion (QAC) aims to help users reach their search intent faster and is a gateway to search for users. Everyday, Billions of keystrokes across 100s of languages are served by Bing Autosuggest in less than 100 ms. The expected suggestions could differ depending on user demography, previous search queries and current trends. In general, the suggestions in the AutoSuggest block are expected to be relevant, personalized, fresh, diverse and need to be guarded against being defective, hateful, adult or offensive in any way. In this video, I broadly talk about different machine learning and deep learning methods for ranking suggestions in a query auto completion (autosuggest) system. Here is the overall agenda 00:00 Ranking suggestions 03:07 Traditional Machine Learning methods for ranking suggestions 14:26 Convolutional Latent Semantic Model 18:38 LSTM encoder For more details, please look at https://drive.google.com/file/d/1h0nxun9nLOKs47p8zfUupVK9pmhiT_24/view Tutorial: Manish Gupta, Puneet Agrawal. Deep Learning Methods for Query Auto Completion. The 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence. July 23-29, 2022. Vienna, Austria.