基于Google云平台的TensorFlow推荐系统

Recommendation Systems with TensorFlow on GCP

553 次查看
Google 云端平台
Coursera
  • 完成时间大约为 11 个小时
  • 难(高级)
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

In this course, you’ll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.

• Devise a content-based recommendation engine
• Implement a collaborative filtering recommendation engine
• Build a hybrid recommendation engine with user and content embeddings

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课程大纲

Recommendation Systems Overview

In this module, we review the scope and plan for the course, define what recommendation systems are, review the different types of recommendation systems and discuss common problems that arise when developing recommendation systems.

Content-Based Recommendation Systems

In this module, we demonstrate how to build a recommendation system using characteristics of the users and items.

COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS

In this module, we show how the data of the interactions between users and items from many different users can be combined to improve the quality of predictions.

Neural Networks for Recommendation Systems

In this module we show how various recommendation systems can be combined as part of a hybrid approach.

Building an End-to-End Recommendation System

In this module we put all the pieces together to build a smart end-to-end workflow for your newly built WALS recommendation model for news articles.

Summary

In this final module, we review what you have learnt so far about recommendation systems and the specialization more broadly.

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