This paper describes a technique for making personalized recommendations from any type of database to a user based on similarities between the interest pro le of that user and those of other users. In particular, we discuss the implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists. Ringo's database of users and artists grows dynamicallyas more people use the system and enter more information. Four di erent algorithms formakingrecommendationsbyusing socialinformation ltering were tested and compared. We present quantitative and qualitative results obtained from the use of Ringo by more than 2000 people.