recommendations improved at the end of the last we used very simple method to build recommendation enginewe used regression to guess ratings value in the first part of this we will continue this work and build more advanced (and betterrating estimator we start with few ideas that are helpful and then combine all of them when combiningwe use regression again to learn the best way to combine them in the second part of this we will look at different way of learning called basket analysiswhere we will learn how to make recommendations unlike the case in which we had numeric ratingsin the basket analysis settingall we have is information about shopping basketsthat iswhat items were bought together the goal is to learn recommendations you have probably already seen features of the form "people who bought also bought yin online shopping we will develop similar feature of our own improved recommendations remember where we stopped in the previous with very basicbut not very goodrecommendation system that gave better than random predictions we are now going to start improving it firstwe will go through couple of ideas that will capture some part of the problem thenwhat we will do is combine multiple approaches rather than using single approach in order to be able to achieve better final performance we will be using the same movie recommendation dataset that we started off with in the last it consists of matrix with users on one axis and movies on the other it is sparse matrixas each user has only reviewed small fraction of the movies