Hands-On Recommendation Systems with Python: Start building powerful and personalized, recommendation engines with Python

Recommendation engines shape what people see, watch, and buy—but building one means choosing the right approach for the data and the problem. This hands-on Python book moves from those foundational choices to practical movie-recommendation examples, including content-based, collaborative, and hybrid methods.

Rounak Banik starts with the basic forms of recommendation and then develops the tools step by step: pandas for data work, data-mining techniques for understanding patterns, and recommender models built around real datasets and use cases.

Start with the recommendation problem

The opening chapter explains the difference between predicting a user’s response to an item and ranking items for that user. It also introduces the main recommender families—collaborative, content-based, knowledge-based, and hybrid—so readers can see how their approaches differ before writing models.

Build from data to recommendations

After a focused introduction to pandas, the book uses a movie-ranking example to put data handling into practice. It then turns to content-based recommenders, using movie plot descriptions and metadata, with techniques including document vectors, TF-IDF, and cosine similarity. The examples show how information about the items themselves can help generate suggestions.

Understand the techniques behind the models

A middle section surveys data-mining methods relevant to recommendation work: similarity measures, clustering, dimensionality reduction, supervised learning, and evaluation metrics. This provides context for the modeling choices explored in the later chapters.

Explore collaborative and hybrid filtering

Using the MovieLens dataset, the book develops user-based and item-based collaborative filters and considers model-based approaches. It closes with a hybrid recommender case study that combines content-based and collaborative filtering techniques—an opportunity to see how complementary signals can be brought together.

A practical starting point for Python developers

The chapter sequence is suited to readers who want to understand recommender systems by following implementations rather than beginning with a heavily theoretical treatment. The publisher identifies Python familiarity as the main prerequisite; some machine-learning knowledge may help, but is not required.

For developers exploring personalization in applications—from movie discovery to social or news experiences—this book offers a grounded route through the concepts and code behind recommendation systems.