System and method for providing personalized recommendations
A system and method for providing a personalized recommendation from a series of partial preferences is presented. A preference distribution of a population including a plurality of weighted ranked lists is identified. A revealed preference of a user is compared to the plurality of ranked lists. An affinity weight between the user and each of the plurality of ranked lists is assigned, and a weighted average of each of the affinity weights is taken.
1. A method for inferring the preferences of a user in relation to a population with a computer comprising a processor and a memory containing non-transitory instructions configured to be executed by the processor, comprising the steps of:
producing a sparse preference distribution of said population comprising a plurality of complete ranked lists and a weight of each of said plurality of ranked lists;
comparing a revealed preference of said user to one of said plurality of ranked lists;
assigning, based upon said comparing, an affinity weight between said user and each of said plurality of ranked lists;
taking a weighted average of each of said affinity weights; and
representing each of said plurality of ranked lists as a two dimensional partial comparison matrix of entries comprising a first index and a second index,
wherein each index indicates an item and each entry of said matrix of entries is set to a first value if said first index item is preferred to said second index item, and otherwise set to a second value if said second index item is preferred to said first index item.
2. The method of claim 1 , wherein said affinity weight between said user and a ranked list is proportional to how similar said revealed preference of said user is to said ranked list.
3. The method of claim 1 , wherein said first value is one and said second value is zero.
4. The method of claim 1 , further comprising the steps of:
receiving, by said computer, a plurality of partial preferences;
learning said sparse preference distribution from said plurality of partial preferences; and
processing said sparse distribution to rank a first preference list against a second preference list.
5. The method of claim 4 , further comprising the step of selecting said plurality of partial preferences from said first preference list and said second preference list.
6. A method for inferring the preferences of a user in relation to a population with a computer comprising a processor and a memory containing non-transitory instructions configured to be executed by the processor, comprising the steps of:
identifying a sparse preference distribution of said population comprising a plurality of ranked lists and a weight of each of said plurality of ranked lists;
comparing a revealed preference of said user to one of said plurality of ranked lists;
assigning, based upon said comparing, an affinity weight between said user and each of said plurality of ranked lists;
taking a weighted average of each of said affinity weights; and
representing each of said plurality of ranked lists as a two dimensional partial comparison matrix of entries comprising a first index and a second index,
wherein each index indicates an item and each entry of said matrix of entries is set to a first value if said first index item is preferred to said second index item, and otherwise set to a second value if said second index item is preferred to said first index item.
7. The method of claim 6 , wherein said first value is one and said second value is zero.