AI Mastery: Recommendation Engines Unleashed

Learn to build knowledge-based and content-based book recommenders in Python, using document vectorizers like tf-idf and count vectorizers, cosine similarity, and pandas.

Build a case study to generate ten book recommendations from a ratings dataset in a Jupyter notebook; load books.csv with windows-1252 encoding and identify features like average rating and pages.

Analyze numerical features with describe to reveal mean, min, max, and counts. Identify the most read and popular books, then apply a weighted rating method using rating counts and percentiles.

Define a weighted score function using rating count v, average rating c, and q; apply it to a books dataframe to produce weighted_score and reveal top ten recommendations.

Build a knowledge-based recommender by asking for genre, page range, year. Use a Jupyter notebook with Python and Pandas to load books and books genre data and melt genres.

Learn to use a melt function to derive a genre variable, filter value==1, drop the value column, and merge df1 with df2 on book id for a clean, ready dataset.

Learn to extract the publication year from dates by converting to pandas to_datetime, handling mixed formats with errors='coerce', and creating a publication year column while dropping the original date.

Develop a knowledge-based recommender function that filters a books data frame by genre, pages, and publication year to generate personalized recommendations.

Develop a knowledge-based recommender by filtering a books dataframe with min and max pages and publication year, then sort by average rating descending for top results.

Explore content-based recommendations by using book descriptions with tf-idf vectors to build a description-driven recommender, featuring data prep, column selection, and description features.

Clean and lowercase the description data, then build a tf-idf vectorizer with English stopwords. Implement a content-based recommender using cosine similarity and a linear kernel to return top ten titles.

Explore building a content-based recommender using tf-idf and cosine similarity, compute similarity scores, sort top matches, and return titles from the computed indices.

Build a two-column recommender by sanitizing authors and genre, using a count vectorizer to create a soup of features, and applying tf-idf with cosine similarity for top-ten recommendations.

Define the soup function to concatenate author, genre, and title into a single feature, apply it to the dataframe, and use count vectorizer with cosine similarity to power a recommender.

Reset the dataframe index and build title-based indices to power a content-based recommender. Implement cosine similarity and derive top recommendations from a title-based index.

Develop a hands-on advanced book recommender by implementing user-based and item-based collaborative filtering and a hybrid model in Python using the surprise package.

Develop a Python-based user-based collaborative filtering workflow to recommend books, loading users, ratings, and genres, handle encoding issues, and prepare a stratified 75/25 train-test split by user for regression.

Explore evaluating a collaborative filtering model using rmse by implementing a mean squared error based score, creating a baseline (rating 3.0), and predicting user-book ratings with scikit-learn.

Build a user-based collaborative filtering system by constructing a ratings matrix with a pivot table, then compute mean and weighted ratings using cosine similarity for personalized recommendations.

Learn to build a user-based weighted mean rating using a cosine similarity matrix, selecting inputs, filtering nulls, and computing the np dot product to predict ratings.

Merge the x_train with the users data frame to incorporate demographics, set the user_id as index, and compute mean book ratings by gender to guide recommendation engines.

Analyze mean ratings by gender for books and define a gender-based collaborative filtering function. Learn user-based and item-based collaborative filtering, using a training matrix and default ratings to compare approaches.

Develop and evaluate model-based collaborative filtering using k-means clustering and the surprise library, applying five-fold cross-validation and SVD to improve rating predictions.

Explore building a hybrid recommendation engine that blends collaborative filtering and content-based methods using SVD with the surprise library, including data preparation, fivefold splits, training, prediction, and id-title mappings.

Load books dataset, clean missing values, and prepare the description text. Import tf-idf vectorizer, remove English stop words, build the tf-idf matrix, and compute cosine similarity with a linear kernel.

Develop a movie recommendation engine by building simple and content-based models using popularity and user behavior. Explore three recommender types: simple, content-based, and collaborative filtering.

Explore essential libraries for building a movie recommendation system, from data manipulation with pandas to NLP preprocessing with nltk, vectorization with tf-idf and count vectorizers, and similarity measures.

Load a movie metadata csv with pandas, inspect the dataset’s shape and features, and create a simple recommender using a weighted rating and a 90th percentile vote-count filter.

Build a simple recommender by filtering movies with high vote counts, applying a weighted rating using V, M, R, and C, and ranking by score to surface top 15 recommendations.

Explore building a content-based movie recommender by computing similarity from descriptions, taglines, cast, and genre, using a subset of data and a vectorizer to generate recommendations.

Vectorize movie descriptions with tf-idf and cosine similarity to generate top ten content-based recommendations, using a reverse title index from the dataset.