there is research around how to detect and avoid these shilling attacks in user-based collaborative filteringbut an even better approach would be to use totally different approach entirely that' not so susceptible to gaming the system that' user-based collaborative filtering againit' simple concept-you look at similarities between users based on their behaviorand recommend stuff that user enjoyed that was similar to youthat you haven' seen yet nowthat does have its limitations as we talked about solet' talk about flipping the whole thing on its headwith technique called itembased collaborative filtering item-based collaborative filtering let' now try to address some of the shortcomings in user-based collaborative filtering with technique called item-based collaborative filteringand we'll see how that can be more powerful it' actually one of the techniques that amazon uses under the hoodand they've talked about this publicly so can tell you that muchbut let' see why it' such great idea with user-based collaborative filtering we base our recommendations on relationships between peoplebut what if we flip that and base them on relationships between itemsthat' what item-based collaborative filtering is understanding item-based collaborative filtering this is going to draw on few insights for one thingwe talked about people being fickletheir tastes can change over timeso comparing one person to another person based on their past behavior becomes pretty complicated people have different phases where they have different interestsand you might not be comparing the people that are in the same phase to each other butan item will always be whatever it is movie will always be movieit' never going to change star wars will always be star warswell until george lucas tinkers with it little bitbut for the most partitems do not change as much as people do sowe know that these relationships are more permanentand there' more of direct comparison you can make when computing similarity between itemsbecause they do not change over time