Can copy-paste behaviour predict film taste?

(A deleted scene from the Practical Recommender Systems book)

People use a computer in different ways – when I need to copy-paste something I always use <ctlr>-c and <ctlr>-v, while my wife insists (how irritating!) on always using the mouse right-click menu. I do not know if such habits can be translated to implicit ratings, unless they could be based on whether to be a geek or not. Just for fun, we could pursue the thought a bit. Let’s pretend that we are looking at films. If we have the Matrix – the geek movie of all time- who would you recommend it to if you only knew how the user performs copy-paste? I would answer the ones using the <ctlr> way. My wife is helping me a lot with this (book), so I should be careful about guessing what kind of films a person who does copy-paste with the right-click menu likes, but I would put her way in between the geek way and the ones doing copy-pasting using the menu in the top of the window.

Asking around, I tried to find members of each group, the ones that use the window menu to copy-paste, the ones that right-click and finally the ones that use only the keyboard. Then I asked them to point at which of three movies they liked more. This was the result of my little survey:

  • <ctrl>-way = The Matrix.
  • Right-click-way = Life of Walter Mitty
  • Window menu-way = You’ve Got Mail


effort to do copy-paste
You can maybe think of it like shown in the figure.

To put this into practice I would need to record a series of copy-paste events from the user, at which point the system could then recommend the movie, which fits to this user’s behaviour.

I hope that this has never been implemented in practice, but if you think about it, then if you have the choice of recommending between “The Matrix” and “You’ve got mail”, the copy-paste behaviour could maybe contribute to give the system a better understanding of what to recommend.

The conclusion is that even if evidence might not be an obvious telltale about users taste, but it might contribute in making the implicit ratings more precise.

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