Chapter 2. Introducing recommenders
Listing 2.1. Recommender input file, intro.csv
Listing 2.2. A simple user-based recommender program with Mahout
Listing 2.3. Configuring and running an evaluation of a recommender
Listing 2.4. Configuring and running a precision and recall evaluation
Listing 2.5. User 5’s preferences in the test data set
Listing 2.6. Changing the evaluation program to run a SlopeOneRecommender
Chapter 3. Representing recommender data
Listing 3.1. Setting preference values in a PreferenceArray
Listing 3.2. Defining input data programmatically with GenericDataModel
Listing 3.3. Triggering a refresh of a recommender system
Listing 3.4. Configuring a JNDI DataSource in Tomcat
Listing 3.5. Configuring a DataSource programmatically
Listing 3.6. Creating and evaluating with Boolean data
Listing 3.7. Evaluating precision and recall with Boolean data
Chapter 4. Making recommendations
Listing 4.1. Revisiting a simple user-based recommender system
Listing 4.2. Updating listing 4.1 to use a custom DataModel for GroupLens
Listing 4.3. Running an evaluation on the simple recommender
Listing 4.4. A simple recommender input file
Listing 4.5. Employing caching with a UserSimilarity implementation
Listing 4.6. The core of a basic item-based recommender
Listing 4.7. Selecting no weighting with a SlopeOneRecommender
Listing 4.8. Creating a JDBC-backed DiffStorage
Listing 4.9. Deploying KnnItemBasedRecommender
Listing 4.10. Creating a cluster-based recommender