limitations of user-based collaborative filtering nowunfortunatelyuser-based collaborative filtering has some limitations when we think about relationships and recommending things based on relationships between items and people and whatnotour mind tends to go on relationships between people sowe want to find people that are similar to you and recommend stuff that they liked that' kind of the intuitive thing to dobut it' not the best thing to dothe following is the list of some limitations of user-based collaborative filteringone problem is that people are fickletheir tastes are always changing somaybe that nice lady in the previous example had sort of brief science fiction action film phase that she went through and then she got over itand maybe later in her life she started getting more into dramas or romance films or romcoms sowhat would happen if my edgy mohawk guy ended up with high similarity to her just based on her earlier sci-fi periodand we ended up recommending romantic comedies to him as resultthat would be bad meanthere is some protection against that in terms of how we compute the similarity scores to begin withbut it still pollutes our data that people' tastes can change over time socomparing people to people isn' always straightforward thing to dobecause people change the other problem is that there' usually lot more people than there are things in your systemso billion people in the world and countingthere' probably not billion movies in the worldor billion items that you might be recommending out of your catalog the computational problem finding all the similarities between all of the users in your system is probably much greater than the problem of finding similarities between the items in your system soby focusing the system on usersyou're making your computational problem lot harder than it might need to bebecause you have lot of usersat least hopefully you do if you're working for successful company the final problem is that people do bad things there' very real economic incentive to make sure that your product or your movie or whatever it is gets recommended to peopleand there are people who try to game the system to make that happen for their new movieor their new productor their new bookor whatever it' pretty easy to fabricate fake personas in the system by creating new user and having them do sequence of events that likes lot of popular items and then likes your item too this is called shilling attackand we want to ideally have system that can deal with that