By Saleem Ansari
- Learn the fundamentals of a suggestion engine and its software in e-commerce
- Discover the instruments and desktop studying equipment required to construct a advice engine
- Explore other forms of advice engines utilizing Scala libraries similar to MLib and Spark
With a rise of knowledge in on-line e-commerce platforms, the demanding situations in supporting clients with narrowing down their seek have grown dramatically. many of the instruments on hand within the Scala surroundings allow builders to construct a processing pipeline to fulfill these demanding situations and create a suggestion procedure to speed up enterprise development and leverage model advocacy in your clients.
This e-book provide you with the Scala wisdom you want to construct a suggestion engine.
You'll be brought to Scala and different comparable instruments to set the degree for the venture and familiarise your self with the various levels within the facts processing pipeline, together with at which phases you could leverage the ability of Scala and comparable instruments. you will additionally notice varied laptop studying algorithms utilizing MLLib.
As the booklet progresses, you are going to achieve certain wisdom of what constitutes a collaborative filtering established advice and discover diversified how to increase clients' recommendation.
What you'll learn
- Discover the instruments within the Scala ecosystem
- Understand the demanding situations confronted in e-commerce platforms and find out how you could clear up these demanding situations with a suggestion engine
- Familiarise your self with desktop studying algorithms supplied via the Apache Spark framework
- Build various types of advice engines from useful code examples
- Enhance the person event by means of studying from consumer feedback
- Dive into a few of the concepts of recommender platforms equivalent to collaborative, content-based, and cross-recommendations
About the Author
Saleem Ansari is a full-stack developer with over eight years of event. He has a different curiosity in computer studying and data retrieval. Having carried out information ingestion and a processing pipeline in center Java and Ruby individually, he is familiar with the demanding situations confronted via large info units in such platforms. He has labored for firms reminiscent of 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) group. He began his trip with FOSS within the 12 months 2004. The very subsequent yr, he shaped JMILUG—Linux clients staff at Jamia Millia Islamia collage, New Delhi. seeing that then, he has been contributing to FOSS by way of organizing group actions and contributing code to varied initiatives (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 firstname.lastname@example.org through e-mail.
Table of Contents
- Introduction to Scala and computer 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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