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Algorithmic recommendation is not simply a higher-resolution representation of a market — a more precise picture of atomistic individuals that does away with the need for larger-scale approximations like market segments. Rather, it is another mode of the synaptic function — another technique for making and interpreting correspondences between persons and things, another way of organizing collective forms. Collaborative filters algorithmically rearticulate the relationship between individual and aggregate traits, suggesting the need for social scientific theories that eschew the classic break between groups and their members (for a preliminary attempt at such an approach, see Latour et al., forthcoming).

The work of recommendation, like the work of demographic marketing, relies on the idea that there are meaningful similarities among consumers and that these similarities correspond with similarities in objects. However, in algorithmic form, these correspondences take on new forms and meanings, blending preference, identity, and similarity. As these theories are built into online infrastructures, shaping the relations between persons and things and articulating new collective forms, they demand attention, not only as material for analysis, but as new modes of analysis itself.

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from “Algorithmic Recommendations and Synaptic Functions”


Via Limn

Notes

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