Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách keo gáy, Bìa mềm
Thể loại:Computers
Năm:2018
In lần thứ:1
Ngôn ngữ:english
Trang:146
Build industry-standard recommender systems
Only familiarity with Python is required
No need to wade through complicated machine learning theory to use this book
Objectives
Get to grips with the different kinds of recommender systems
Master data-wrangling techniques using the pandas library
Building an IMDB Top 250 Clone
Build a content based engine to recommend movies based on movie metadata
Employ data-mining techniques used in building recommenders
Build industry-standard collaborative filters using powerful algorithms
Building Hybrid Recommenders that incorporate content based and collaborative fltering
About
Recommendation
systems are at the heart of almost every internet business today; from
Facebook to Netflix to Amazon. Providing good recommendations, whether
it's friends, movies, or groceries, goes a long way in defining user
experience and enticing your customers to use your platform.
This
book shows you how to do just that. You will learn about the different
kinds of recommenders used in the industry and see how to build them
from scratch using Python. No need to wade through tons of machine
learning theory—you'll get started with building and learning about
recommenders as quickly as possible..
In this book, you will build an
IMDB Top 250 clone, a content-based engine that works on movie
metadata. You'll use collaborative filters to make use of customer
behavior data, and a Hybrid Recommender that incorporates content based
and collaborative filtering techniques
With this book, all you need
to get started with building recommendation systems is a familiarity
with Python, and by the time you're fnished, you will have a great grasp
of how recommenders work and be in a strong position to apply the
techniques that you will learn to your own problem domains.
