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Matthew Peters: Distributed representations of keywords, web sites and pages

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Published on Sep 7, 2016

Matthew Peters

Title: Distributed representations of keywords, web sites and pages

Abstract: Distributed representations of words, phrases and sentences are central to recent advances in machine translation, language modeling, semantic similarity, and other tasks. In this talk, I'll explore ways to learn similar representations of search queries, web pages and web sites. The first portion of the talk describes a method to learn a keyword-web page similarity function applicable to web search. It represents the web page as a set of attributes (URL, title, meta description tag, etc) and uses a separate LSTM encoder for each attribute. The network is trained end-to-end from clickthrough logs. The second half of the talk introduces a measure of authority for each web page and jointly learns keyword-keyword, keyword-site and keyword-site-authority relationships. The multitask network leverages a shared representation for keywords and sites and learns a fine grained topic authority (for example politico.com is an authority on the topic "Bernie Sanders" but not on "Seattle Mariners").

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