Abstract: A computer-implemented service recommends digital works (and/or creators of works) to a user based on works currently or previously played or downloaded by the user on a player device or based on playlists stored on the player device. The works may be, for example, music files, video files, electronic books, or other digital content for playing by users. A user may thus obtain personalized recommendations that are based on works obtained from sources (web sites, physical CDs, etc.) that are independent of the recommendations system. In one embodiment, the service identifies pairs of works (and/or work creators) that are similar to each other by virtue of the relatively high frequency with which they co-occur on playlists or within play histories of users. The resulting mappings are used to provide recommendations to users.

Abstract: An interactive system enables users to generate and submit lists of items (e.g., products selected from an electronic catalog) for viewing by other users. The user-generated lists are selectively suggested to users of the system based (or based in part) on ratings of the user-generated lists. These ratings may, for example, be based on explicit ratings of the lists by users, and/or measures of how effective the respective lists are at driving purchases of items. In some contexts, the system may select one or more user-generated lists to present to a user based on a set of items purchased, viewed, or otherwise selected by the user, or based on the results of a search query submitted by the user.

Abstract: A computer-implemented service recommends digital works (and/or creators of works) to a user based on works currently or previously played or downloaded by the user on a player device or based on playlists stored on the player device. The works may be, for example, music files, video files, electronic books, or other digital content for playing by users. A user may thus obtain personalized recommendations that are based on works obtained from sources (web sites, physical CDs, etc.) that are independent of the recommendations system. In one embodiment, the service identifies pairs of works (and/or work creators) that are similar to each other by virtue of the relatively high frequency with which they co-occur on playlists or within play histories of users. The resulting mappings are used to provide recommendations to users.