Recommendation Engines
While there is no doubt a need for recommendation within information systems that are growing in volume and traffic as fast as they are. Recommendation can be thought of in a sense as subjective filtering. Taking the common example of Netflix as a model. Offering approximately 100,000 titles on DVD, each on average 2 hours long.. works out to about 23 years of continuous viewing were you to try to personally evaluate each title… which would be impractical for even the most devoted cinephile. The interesting part of the problem is that within any recommendation system we are attempting an algorithm that can subjectively filter a volume of information that is itself not static (that is, it is constantly being updated with new releases and added titles) toward an individual that is also not static… people’s behavior can change over time. So accounting for what is a massively dynamic and changing system is no small feat. Assuming that you could account for multi-nodal change efficiently… being able to make suggestions to users on new or previously unavailable information… accounting for how that information might effect successive iterations of interest and recommendation… I would be inclined to say that such a system, in its optimal form, would accelerate cultural consumption toward a bottle-neck of cultural production. A good problem to have if you are in the business of producing culture.
What is more accurately the case is that at present our recommendation systems make some significant assumptions about their users… namely: that users don’t change very much (this is arguable on different scopes of human behavior), that people know what they want… or more appropriate to the subject of recommendation… they know what they like. This can be taken in at least one of two ways… You could take the vantage from high above like that of Edward Bernays (“father of public relations” and modern advertising) –”It is sometimes possible to change the attitudes of millions but impossible to change the attitude of one man.” It’s hard to say whether the advent of the internet would have made him reconsider the latter statement. Inspired by his uncle’s (Sigmund Freud) ideas about human desire and the subconscious mind… his was a view in which people could be manipulated to desire things that they might not have previously by sublimation and association. But possibly more valuable in an ever more stratified media landscape is considering how an individual user can be understood in algorithmic terms. Tendencies can drift toward either trait based analysis and attribution or situational models entirely… known as Fundamental attribution error… Only a system that accounts for an individual user’s situation and his or her specific traits holistically, neither of which remain static.. not even their genetic code, will be truly effective. How active the system is in attempting to alter a user’s immediate situation or exploit known traits toward 3rd party interests places that system somewhere nearer or further away from Bernay’s style manipulation.
Where I tend to fixate however is the idea of discovery and the psychology of it. The feeling of having ownership over something because it was in some contrived instance perhaps seen first by you. I recall during the days of alternative and “grunge” that there was often a notion of an elusive threshold, that once crossed, a band’s contingent… the very same people that had “discovered” them and instilled them with exclusive value could no longer maintain their grasp… alas as the band hurled forth transforming into a “sell out”. We dance with this ephemeral quality of scarcity. A question that I often ask is whether or not moments of discovery could be effectively simulated.. and what that might mean for the user and their experience of information. On a somewhat flamboyant level Wikileaks might be doing this.
Returning to Netflix for a moment… I have to admit that I was pleasantly surprised once with their recommendation, discovering a film that I would easily put in my top 10: Primer by Shane Carruth.
A variation on recommendation that centers on the effects of choice in different conditions is also relevant. See Sheena Iyengar‘s Ted talk “The Art of Choice” or book by the same title. The idea of customization as a means of making choices more personally meaningful is discussed.
See also Century of the Self by Adam Curtis.