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Tag Archives: Algorithms

The Numerics of Style. Part III: (in)Dividuals

Historically, music has always been linked to a personal presence; its performance in festivals, or in people’s homes, or in concert settings, speaks to music’s relation-based nature. Music’s only materiality is based in the pathway it forges between the listener and the performer—it is the acoustic material of two-way conversation. The invention of recorded media allowed this materiality to be subsumed by a physical product. In keeping with the capitalist mode of society, an industry emerges in service to the circulation of music-as-object: the 33RPM disc, the cassette, the CD, the mp3. Capitalism used to have a way of hindering and supporting the entertainment industry at the same time: the industry is burdened by the high costs incurred in production of the object, but those high costs also protect the industry’s position in the marketplace by imposing a barrier to entry by potential competitors. This fragile balance has changed in recent decades now that the costs of producing the music-as-object have been virtually eliminated, and the music industry can no longer sustain itself by the revenues from industrial production alone. Thus, the suggestion algorithm’s use in the marketing of musical products represents much more than a struggle for legitimacy. It represents a new process of fragmentation in society at large, and signals the end of industrial production as we know it.

This process of fragmentation represents a Deleuzian distopia made real. The desire to translate the ephemeral ideas of musicality, and why certain music attributes appeal to certain people, The goal of the suggestion algorithm is “universal modulation,” a method of translating phenomena into a readable data, thus achieving an approximation of the original idea that can be quantized, marketed, and capitalized upon. Gilles Deleuze introduces the idea of humans as “dividuals,” entities comprised of multiple parcels of data that can be bought, sold, and traded in the new marketplace. In previous centuries, the individual was “one of many,” a member of a larger mass. Today’s dividual is known by his password, a code that can grant or restrict access to information1. This new conceptualization recasts the each of us as a user, an aggregate of recorded preferences, histories, and proclivities. Every interaction with the market is thus mediated by the password: the human existence is constantly reconfigured in accordance to the user’s associated dataset.

However, this fragmentation process does not have to be viewed so pessimistically. Theorist Yochai Benkler posits that, as it applies to market architecture, fragmentation (or decentralization) can be healthy. Benkler described this sea change in a talk on “open-source economics” given at the 2005 TED conference. The industry can survive this dramatic shift by employing adaptive strategies that harness this threat.

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A recommender system employing methods of collaborative filtering is an example of such an interface: each user provides invaluable data, and thus effectively markets the products to other users. The system provides the framework to sponsor a conversation about music between music enthusiasts. Users, as they share media, are willing to put in collaborative effort to make the web a better, more efficient place. For example, as the user base of Last.fm grows in number, so grows the number of linkages between users, and thus the opportunities to encounter new products increase.


Footnotes:
1 Deleuze, Gilles. “Postscripts to the Societies of Control.” May 1990, L’Autre journal, no. 1.
http://www.watsoninstitute.org/infopeace/vy2k/deleuze-societies.cfm

The Numerics of Style. Part II: “The Problem is YOU!” Function U, Explicit and Implicit Preferences, and Harlo’s IDM Ghetto

Suggestion algorithms are part of the broader science of recommendation systems, an incredibly cross-disciplinary thread in computer science that strives to bridge statistics, network theory, computer learning, cognitive science, and marketing. Recommendation systems first appeared in the mid-1990’s as computer scientists began to discover the immense profitability in perfecting the search algorithm. Vendors in every sector started to demand that their servers incorporate recommendation capabilities into the servers that held their virtual store.

Recommendation systems vary one from another, but they all revolve around the following scenario: Imagine a digital vendor that has a finite set of items to sell (set S) and a potential consumer base (set C). Upon visiting the vendor’s website, a user should be directed to a subset (R) of items of high “usefulness” to him. The vendor’s server must employ an optimization function (function U) that, according to how it’s authored, is capable of generating an ordered subset of items, ranked by their usefulness, “on the fly.1

u: C x S → R
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The Numerics of Style. Part 1: “Panic Algorithm”

(So sorry I forgot to post on Thursday— I won’t forget again!)

Question:
The first part of my investigation into suggestion algorithms answers the following question: How did the suggestion algorithm emerge as the culture industry’s main weapon in the fight to retain its legitimacy?
numerics_of_style

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The Numerics of Style: Individuation and Suggestion Algorithms

My next travelogue will be dedicated to the study of suggestion algorithms in online music services. I will take an in-depth look at three popular intelligent music-sharing platforms: Last.fm, Pandora, and The Echo Nest, and perform a technical, economical, and theoretical analysis of each.

In this travelogue, I will first discuss each platform’s technical approaches to data acquisition and methods of data analysis. These three examples employ radically different models, and various network topologies can be found in their differences. Next, I will move on to the broader topic of marketability. Each of these platforms have two sides to them: they are in service to public entertainment, but they also offer themselves as a marketing tool for the music industry. I would like to examine how this double-sided approach both radicalizes market research in the entertainment industry, and borrows from more traditional strategies. Finally, I will read these platforms with the Deleuzian concept of (in)dividualism in so-called “societies of control.” This module will examine how these three platforms exemplify the practice of “universal modulation” within our society, and how our particular capitalist age strives to turn such ephemeral human concepts into discrete packets of data.

Suggested readings can be found on my delicious under the tag “numerics_of_style” (and they’ll also pop up on the class’ TDMCC tag feed).

If anyone has any suggestions for readings for either the economic, technical, or philosophical thread of my travelogue, I would love, love, love the suggestion. And any critique along the way would be greatly appreciated. This should be a pretty fun, yet challenging, topic to tackle!