document: Naïve filterbots for robust cold-start recommendations The goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users . Given detailed enough history , item-based collaborative filtering ( CF ) often performs as well or better than almost any other recommendation method . However , in cold-start situations - where a user , an item , or the entire system is new - simple non-personalized recommendations often fare better . We improve the scalability and performance of a previous approach to handling cold-start situations that uses filterbots , or surrogate users that rate items based only on user or item attributes . We show that introducing a very small number of simple filterbots helps make CF algorithms more robust . In particular , adding just seven global filterbots improves both user-based and item-based CF in cold-start user , cold-start item , and cold-start system settings . Performance is better when data is scarce , performance is no worse when data is plentiful , and algorithm efficiency is negligibly affected . We systematically compare a non-personalized baseline , user-based CF , item-based CF , and our bot-augmented user - and item-based CF algorithms using three data sets ( Yahoo ! Movies , MovieLens , and EachMovie ) with the normalized MAE metric in three types of cold-start situations . The advantage of our `` naïve filterbot '' approach is most pronounced for the Yahoo ! data , the sparsest of the three data sets .
doc_bio_tags: B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O