Here i want to share one of the possible solutions of the discoverability problem, described in : "The Discoverability Problem: How To Get Out of the Filter Bubble Recommendation Systems?" (http://curation.masternewmedia.org/p/2955743421/the-discoverability-problem-how-to-get-out-of-the-filter-bubble-recommendation-systems via Robin Good):
I know one well-working recommendation service (books, movies, music). It is based on social networking, collaborative filtering, reviewing of professional reviewers/librarians and members of this social network.
The music industry was first where such ideas were implemented, for example Last.Fm (ex- Audioscrobbler), and many others. Also industry has done great job to create music metadata databases - MusicBrainz, Gracenote, etc.
It is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The motivation for collaborative filtering comes from the idea that people often get the best recommendation from someone with similar taste. Collaborative filtering explores techniques for matching people with similar interests and making recommendations on this basis.
Collaborative filtering algorithms often require:
1) users’ active participation,
2) an easy way to represent users’ interests to the system,
3) algorithms that are able to match people with similar interests.
Typically, the workflow of a collaborative filtering system is:
- A user expresses his or her preferences by rating items (eg. books, movies or CDs) of the system. These ratings can be viewed as an approximate representation of the user's interest in the corresponding domain.
- The system matches this user’s ratings against other users’ and finds the people with most “similar” tastes.
- With similar users, the system recommends items that the similar users have rated highly but not yet being rated by this user (presumably the absence of rating is often considered as the unfamiliarity of an item).
I took part in the development and launch of this recommendation system (2004 - 2008). It is called IMHONET (http://imhonet.ru), 5 mln visitors per month. It is independent from publishers and online sellers.