Thursday, November 17, 2016

Tracing Literary Influence Via Commercial Algorithm

Reading the Cordell, I was captivated by Ed Finn's project: Using Amazon reviews and recommendations to trace popular reading tastes within a contemporary context. I think this approach could be deepened and expanded to trace literary influence as it pertains to popular taste across a range of books/authors. In other words, I propose a project akin to Six Degrees of Separation. If, for instance, I were to take Sylvia Plath’s Ariel and take the top five poetry and/or fiction recommendations written by other authors (reader’s also liked; related books; customer’s also purchased, etc.) what links could I find between genres, authors, literary style, and/or literary eras. I would also propose cross-checking these recommendations across several sites, including Google Books, Amazon, GoodReads, and Powells, and others. This would not only speak to differences in site algorithms but differences in taste among site users.  Would any common threads emerge? Would any glaring oddities emerge? What explanations could be made for these outliers and commonalities?


Furthermore, could we discover links between genres and authors that are not typically explored in an academic and critical context, and what might these overlooked links reveal about the works in question?

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