Deep Learning for Search and Recommender Systems in Practice

Abstract

In this talk, we will go over the components of personalized search and recommender systems and demonstrate the applications of various deep learning techniques along the way. Search and recommender systems are probably the most prevalent ML powered application across the industry. They share most of the components composition and provide a user a ranked list of items, while there is subtle difference that a search system typically acts passively with a clear user intention in terms of queries and a recommender system acts more proactively. Deep learning has been wildly successful in solving complex tasks such as image recognition, speech recognition, natural language processing and understanding, machine translation, etc. In the area of personalized recommender systems, deep learning has been showing tremendous impact in recent years. Search and recommender systems can be staged roughly in three phases: 1. User and query understanding, where a query or a user profile are processed so that the systems can use the processed information to 2. retrieve all the related items (high recall) and 3. rank the items by the order of the most relevance to the user's intent (high precision). Each phase has its unique challenges but deep learning has been ubiquitously pushing beyond the limit. After walking through the talk, we hope the audience would gain some first-hand experience building a personalized search/recommender system using deep learning techniques.