the other advantage is that there are generally fewer things that you're trying to recommend than there are people you're recommending to so again billion people in the worldyou're probably not offering billion things on your website to recommend to themso you can save lot of computational resources by evaluating relationships between items instead of usersbecause you will probably have fewer items than you have users in your system that means you can run your recommendations more frequentlymake them more currentmore up-to-dateand betteryou can use more complicated algorithms because you have less relationships to computeand that' good thingit' also harder to game the system sowe talked about how easy it is to game user-based collaborative filtering approach by just creating some fake users that like bunch of popular stuff and then the thing you're trying to promote with item-based collaborative filtering that becomes much more difficult you have to game the system into thinking there are relationships between itemsand since you probably don' have the capability to create fake items with fake ties to other items based on manymany other usersit' lot harder to game an item-based collaborative filtering systemwhich is good thing while ' on the topic of gaming the systemanother important thing is to make sure that people are voting with their money general technique for avoiding shilling attacks or people trying to game your recommender systemis to make sure that the signal behavior is based on people actually spending money soyou're always going to get better and more reliable results when you base recommendations on what people actually boughtas opposed to what they viewed or what they clicked onokayhow item-based collaborative filtering worksalrightlet' talk about how item-based collaborative filtering works it' very similar to user-based collaborative filteringbut instead of userswe're looking at items solet' go back to the example of movie recommendations the first thing we would do is find every pair of movies that is watched by the same person sowe go through and find every movie that was watched by identical peopleand then we measure the similarity of all those people who viewed that movie to each other soby this means we can compute similarities between two different moviesbased on the ratings of the people who watched both of those movies