Abhishek Thakur - Classifying Search Queries Without User Click Data
2,571 views · Published 31 May 2016 · 34:25 · Indexed 22 October 2026
Channel: PyData · 2016 · Science & Technology
PyData Berlin 2016 This talk discusses how machine learning/data mining techniques can be applied to classify search terms that people use in search engines like Google, Bing, Yahoo etc. The talk focuses on machine learning techniques such as LSTM (long short term memory) rather than traditional ways like analysing user-behaviour with the help of their search logs. Traditionally, search queries are classified into different categories by analysing user-behavior with the help of search logs. Instead of focussing on the search logs and by analysing queries with the help of machine learning models such as LSTM, it is possible to get a very decent model to classify the search queries. This talk focuses majorly on LSTMs and their usefulness when it comes to search query classification. We also discuss how we can accurately classify search queries in hundreds of categories using open source data available online and how this can be combined with LSTMs to provide a much stable and better result. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
More from this channel
-
31:23
Kostas Perifanos - Authorship Attribution Using Python
-
31:08
Prash Majmudar - Measuring the Digital Economy Using Big Data
-
36:26
Ian Ozsvald - The High Performance Python Landscape
-
37:48
Mike Muller - Shared Memory Parallelism with Python
-
35:01
Martin Goodson - Most Winning A/B Test Results are Illusory
-
30:19
Mark Basham - Python for High Throughput Science
-
1:13:42
David Cournapeau & Simon Jagoe - Narrowing the Gap Between Array Computing and Databases
-
31:38
Emlyn Clay - Phython, Pharmaceuticals & Discovery