The document discusses recommendation algorithms and their relationship to products. It describes different types of recommendation algorithms like user-based, item-based, content-based, and hybrid algorithms. It also provides examples of datasets used for developing and evaluating recommendation algorithms.
12. Binary dataset user_base item_base conten_base other apriori Bob 1 0 0 0 0 Linda 1 1 0 0 0 Lucy 0 0 1 1 1 Tom 0 1 0 0 1 Peter 0 0 0 1 1
13. Dispatcher User ID Other user ID Algorithm Similarity Bob Linda 0.589723 Bob Tom 0.279055 Linda Tom 0.279055 Lucy Tom 0.227848 Lucy Peter 0.481507 Tom Peter 0.279055
14. User base User id User id Similarity Bob Linda 0.279055 Bob Lucy 0.416997 Linda Lucy 0.197322 Linda Tom 0.310667 Linda Tom 0.219675 Lucy Peter 0.219675