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I am trying to match objects based on predefined user preferences. A simple example would be finding best matching vechicle.

Lets say a user 'Tom' is offered a rented vehicle for travel based on his predefined preferences. In this case, the predefined user preferences will be -

** Pre-defined user preferences for Tom:

PreferredVehicle (Make='ANY', Type='3-wheeler/4-wheeler',
Category='Sedan/Hatchback', AC/Non-AC='AC')

** while the 10 available vehicles are -

Vechile1(Make='Toyota', Type='4-wheeler', Category='Hatchback', AC/Non-AC='AC')
Vechile2(Make='Tata', Type='3-wheeler', Category='Transport', AC/Non-AC='Non-AC')
Vechile3(Make='Honda', Type='4-wheeler', Category='Sedan', AC/Non-AC='AC')
;
;
and so on upto 'Vehicle10'

All I want to do is - choose a vehicle for Tom that best matches his preferences and also probably give him choices in order, i.e. best match first.

Questions I have :

  1. Can this be done with Mahout Taste?
  2. If yes, can someone please point me to some example code where I can start quickly?
Taryn
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Gopi
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1 Answers1

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A recommender may not be the best tool for the job here, for a few reasons. First, I don't expect that the best answers are all that personal in this domain. If I wanted a Ford Focus, the best alternative you have is likely about the same for most every user. Second, there is not much of a discovery problem here. I'm searching for a vehicle that meets certain needs; I don't particularly want or need to find new and unknown vehicles, like I would for music. Finally you don't have much data per user; I assume most users have never rented before, and very few have even 3+ rentals.

Can you throw this data at a recommender anyway? Sure, try Mahout Taste (I'm the author). If you have the book Mahout in Action it will walk you through it. Since it's non-rating data, I can also recommend the successor project, Myrrix (http://myrrix.com) as it will be easier to set up and run. You can at least evaluate the results to see if it's anywhere near useful.

Either way, your work will just be to make a CSV file of "userID,vehicleID" pairs from your data and feed it in. Then it will give you vehicle IDs as recommendations for any user ID.

But, I imagine you will do much better to analyze what people picked when the car wasn't available, and look at the difference, and learn which attributes they are most and least likely to be sacrificed, and learn to score the alternatives that way. This is entirely feasible since this data set is small, and because you have rich item attribute data.

Sean Owen
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