By Saleem Ansari
- Learn the fundamentals of a suggestion engine and its software in e-commerce
- Discover the instruments and laptop studying equipment required to construct a suggestion engine
- Explore other forms of advice engines utilizing Scala libraries resembling MLib and Spark
With a rise of knowledge in on-line e-commerce structures, the demanding situations in supporting clients with narrowing down their seek have grown dramatically. a few of the instruments to be had within the Scala atmosphere allow builders to construct a processing pipeline to fulfill these demanding situations and create a suggestion process to speed up enterprise development and leverage model advocacy in your clients.
This publication will give you the Scala wisdom you want to construct a advice engine.
You'll be brought to Scala and different similar instruments to set the level for the undertaking and familiarise your self with the several levels within the info processing pipeline, together with at which levels you could leverage the facility of Scala and similar instruments. you are going to additionally observe assorted computer studying algorithms utilizing MLLib.
As the publication progresses, you'll achieve certain wisdom of what constitutes a collaborative filtering established advice and discover assorted ways to enhance clients' recommendation.
What you'll learn
- Discover the instruments within the Scala ecosystem
- Understand the demanding situations confronted in e-commerce structures and learn the way you could remedy these demanding situations with a advice engine
- Familiarise your self with computer studying algorithms supplied via the Apache Spark framework
- Build assorted types of advice engines from sensible code examples
- Enhance the person adventure via studying from person feedback
- Dive into many of the suggestions of recommender platforms similar to collaborative, content-based, and cross-recommendations
About the Author
Saleem Ansari is a full-stack developer with over eight years of adventure. He has a unique curiosity in laptop studying and data retrieval. Having carried out information ingestion and a processing pipeline in middle Java and Ruby individually, he understands the demanding situations confronted via large facts units in such platforms. He has labored for corporations equivalent to pink Hat, Impetus applied sciences, Belzabar software program, and Exzeo software program. he's additionally a passionate member of unfastened and open resource software program (FOSS) neighborhood. He all started his trip with FOSS within the 12 months 2004. The very subsequent 12 months, he shaped JMILUG—Linux clients team at Jamia Millia Islamia college, New Delhi. for the reason that then, he has been contributing to FOSS by way of organizing neighborhood actions and contributing code to varied tasks (for additional information, stopover at http://github.com/tuxdna). He additionally mentors scholars approximately FOSS and its benefits.
In 2015, he reviewed books regarding Apache Mahout, specifically studying Apache Mahout and Apache Mahout necessities; either the books have been produced through Packt Publishing.
He blogs at http://tuxdna.in/ and will be reached at email@example.com through e-mail.
Table of Contents
- Introduction to Scala and laptop Learning
- Data Processing Pipeline utilizing Scala
- Conceptualizing an E-Commerce Store
- Machine studying Algorithms
- Recommendation Engines and the place They healthy in?
- Collaborative Filtering as opposed to Content-Based suggestion Engines
- Enhancing the person Experience
- Learning from consumer Feedback
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Building a Recommendation Engine with Scala by Saleem Ansari