techniques simple recommender system will look for customers who've rented similar movies as you have and then suggest those that the others have watched but you haven' seen yet this technique is called -nearest neighbors in machine learning customer who behaves similarly to you isn' necessarily the most similar customer you'll use technique to ensure that you can find similar customers (local optimawithout guarantees that you've found the best customer (global optimuma common technique used to solve this is called locality-sensitive hashing good overview of papers on this topic can be found at the idea behind locality-sensitive hashing is simpleconstruct functions that map similar customers close together (they're put in bucket with the same labeland make sure that objects that are different are put in different buckets central to this idea is function that performs the mapping this function is called hash functiona function that maps any range of input to fixed output the simplest hash function concatenates the values from several random columns it doesn' matter how many columns (scalable input)it brings it back to single column (fixed outputyou'll set up three hash functions to find similar customers the three functions take the values of three moviesthe first function takes the values of movies and the second function takes the values of movies and the last function takes the values of movies and this will ensure that the customers who are in the same bucket share at least several movies but the customers inside one bucket might still differ on the movies that weren' included in the hashing functions to solve this you still need to compare the customers within the bucket with each other for this you need to create new distance measure the distance that you'll use to compare customers is called the hamming distance the hamming distance is used to calculate how much two strings differ the distance is defined as the number of different characters in string table offers few examples of the hamming distance table examples of calculating the hamming distance string string hamming distance hat cat hat mad tiger tigre paris rome