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Top 4 Recommendation Engine Books



The AI Optify data team writes about topics that we think data engineers and data scientists will love. AI Optify has affiliate partnerships so we may get a share of the revenue from your purchase.

Top Recommendation Engine Books - For this post, we have scraped various signals (e.g. online ratings/reviews, topics covered, author social influence in the field, year of publication, social media mentions, etc.) for more than 10 Recommender Systems books from web. We have fed all above signals to a trained Machine Learning algorithm to compute a score and rank the top books.

The readers will love our list because it is Data-Driven & Objective. Enjoy the list:


1. Programming Collective Intelligence: Building Smart Web 2.0 Applications
$25

With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general--all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application.


2. Recommender Systems: An Introduction
$77

This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory.


3. Building a Recommendation Engine with Scala
$35

This book provides you with the Scala knowledge you need to build a recommendation engine. You'll be introduced to Scala and other related tools to set the stage for the project and familiarise yourself with the different stages in the data processing pipeline, including at which stages you can leverage the power of Scala and related tools. You'll also discover different machine learning algorithms using MLLib. As the book progresses, you will gain detailed knowledge of what constitutes a collaborative filtering based recommendation and explore different methods to improve users' recommendation.


4. Mining of Massive Datasets
$58

This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.