What this book covers
Chapter 1, Association Rule Mining, builds recommender systems with transaction data. We identify cross-sell and upsell opportunities.
Chapter 2, Fuzzy Logic Induced Content-Based Recommendation, addresses the cold start problem in the recommender system. We handle the ranking problem with multi-similarity metrics using a fuzzy sets approach.
Chapter 3, Collaborative Filtering, introduces different approaches to collaborative filtering for recommender systems.
Chapter 4, Taming Time Series Data Using Deep Neural Networks, introduces MXNet R, a package for deep learning in R. We leverage MXNet to build a deep connected network to predict stock closing prices.
Chapter 5, Twitter Text Sentiment Classification Using Kernel ...
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