EPJ Data Science Highlight - Discovering temporal regularities in retail customers’ shopping behaviour

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Published on Wednesday, 18 April 2018 08:26

Why do we buy certain items when we buy them? A new study published in EPJ Data Science analyzes personal retail data to extract a temporal purchasing profile, which is able to summarize whether and when a customer makes a purchase. Its results show that certain patterns and types of shoppers are detectable, which can be used both by customers to enable personalized services, and by the retail market chain for providing offers and discounts tailored to the individual shoppers personal temporal profile.

(Guest post by Riccardo Guidotti and Anna Monreale, originally published on the SpringerOpen blog)

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EPJ H Highlight - Once upon a time, an exoplanet was discovered

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Published on Monday, 16 April 2018 13:26

Personal recollections of an astrophysicist shed new light on the 1995 discovery on 51 Pegasi b

In recent history, a very important achievement was the discovery, in 1995, of 51 Pegasi b, the first extrasolar planet ever found around a normal star other than the Sun. In a paper published in EPJ H, Davide Cenadelli from the Aosta Valley Astronomical Observatory (Italy) interviews Michel Mayor from Geneva Observatory (Switzerland) about his personal recollections of discovering this exoplanet. They discuss how the development of better telescopes made the discovery possible. They also delve into how this discovery contributed to shaping a new community of scholars pursuing this new field of research. In closing, they reflect upon the cultural importance that the 51 Pegasi b discovery had in terms of changing our view of the cosmos.

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EPJ Data Science Highlight – The science behind what makes a bestselling book

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Published on Tuesday, 10 April 2018 08:06

Books that are fiction, thrillers or mysteries, have high initial sales numbers and are released around Christmas are more likely to be bestsellers, according to a study published in EPJ Data Science

(This post was originally published on the SpringerOpen blog)

A team of researchers from Northeastern University, Boston, used a big data approach to investigate what makes a book successful. By evaluating data from the New York Times Bestseller Lists from 2008 to 2016, they developed a formula to predict if a book would be a bestseller.

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