SYSTEM AND METHOD FOR LOW RANK FIELD-WEIGHTED FACTORIZATION MACHINE AND APPLICATION THEREOF IN CONTENT RECOMMENDATION

1 . A method, comprising:

processing ad auction related data to identify supply data and demand data;

determining a diagonal vector d based on the supply data and the demand data;

computing a predicted performance metric via low rank field weighted factorization machines for each of a plurality of candidate ads included in the ad auction related data based on the diagonal vector d;

ranking the plurality of candidate ads based on their corresponding predicted performance metrics; and

selecting one of the ranked plurality of candidate ads as a winning ad according to a predetermined selection criterion and outputting the winning ad for display via an online platform, so as to support online advertising with real-time performance-based ad ranking and recommendation.

2 . The method of claim 1 , wherein:

the supply data is associated with a user and context information related to a display ad opportunity associated with the ad auction; and

the demand data is associated with the plurality of candidate ads and includes information characterizing each of the plurality of candidate ads.

3 . The method of claim 1 , wherein the determining the diagonal vector d comprises:

accessing the supply data and the demand data;

determining a first matrix U and a transpose matrix UT thereof;

obtaining a vector e and a corresponding square diagonal matrix diag(e); and

computing a diagonal vector d based on UT diag(e) U, wherein

U and e are learned via machine learning based on training data.

4 . The method of claim 1 , wherein the computing the predicted performance metric for the candidate ad via low rank field weighted factorization machines comprises:

extracting the supply data from the ad auction related data;

based on the extracted supply data,

computing a first supply related matrix US, and

computing a second supply related matrix VS.

5 . The method of claim 4 , further comprising:

extracting the demand data from the ad auction related data; and

based on the extracted demand data,

computing a first demand related matrix UD, and

computing a second demand related matrix VD.

6 . The method of claim 5 , further comprising computing a P matrix based on the first supply related matrix US, the second supply related matrix VS, the first demand related matrix UD, and the second demand related matrix VD.

7 . The method of claim 6 , further comprising computing, for the candidate ad, the predicted performance metric based on the P matrix and the diagonal vector d.

8 . A machine readable and non-transitory medium having information recorded thereon, wherein the information, when read by the machine causes the machine to perform the following steps:

processing ad auction related data to identify supply data and demand data;

determining a diagonal vector d based on the supply data and the demand data;

computing a predicted performance metric via low rank field weighted factorization machines for each of a plurality of candidate ads included in the ad auction related data based on the diagonal vector d;

ranking the plurality of candidate ads based on their corresponding predicted performance metrics; and

selecting one of the ranked plurality of candidate ads as a winning ad according to a predetermined selection criterion and outputting the winning ad for display via an online platform, so as to support online advertising with real-time performance-based ad ranking and recommendation.

9 . The medium of claim 8 , wherein:

the supply data is associated with a user and context information related to a display ad opportunity associated with the ad auction; and

the demand data is associated with the plurality of candidate ads and includes information characterizing each of the plurality of candidate ads.

10 . The medium of claim 8 , wherein the determining the diagonal vector d comprises:

accessing the supply data and the demand data;

determining a first matrix U and a transpose matrix UT thereof;

obtaining a vector e and a corresponding square diagonal matrix diag(e); and

computing a diagonal vector d based on UT diag(e) U, wherein

U and e are learned via machine learning based on training data.

11 . The medium of claim 8 , wherein the computing the predicted performance metric for the candidate ad via low rank field weighted factorization machines comprises:

extracting the supply data from the ad auction related data;

based on the extracted supply data,

computing a first supply related matrix US, and

computing a second supply related matrix VS.

12 . The medium of claim 11 , wherein the information, when read by the machine, further causes the machine to perform the following steps:

extracting the demand data from the ad auction related data; and

based on the extracted demand data,

computing a first demand related matrix UD, and

computing a second demand related matrix VD.

13 . The medium of claim 12 , wherein the information, when read by the machine, further causes the machine to perform the step of computing a P matrix based on the first supply related matrix US, the second supply related matrix VS, the first demand related matrix UD, and the second demand related matrix VD.

14 . The medium of claim 13 , wherein the information, when read by the machine, further causes the machine to perform the step of computing, for the candidate ad, the predicted performance metric based on the P matrix and the diagonal vector d.

15 . A system, comprising:

a low rank field-weighted factorization machine (FwFM) predicted performance (P-P) metric determiner implemented by a processor and configured for

processing ad auction related data to identify supply data and demand data,

determining a diagonal vector d based on the supply data and the demand data, and

computing a predicted performance metric via low rank FwFM for each of a plurality of candidate ads included in the ad auction related data based on the diagonal vector d;

a P-P metric based ad ranking unit implemented by a processor and configured for ranking the plurality of candidate ads based on their corresponding predicted performance metrics; and

a winning ad selection unit implemented by a processor and configured for selecting one of the ranked plurality of candidate ads as a winning ad according to a predetermined selection criterion and an output device implemented by a processor and configured for outputting the winning ad for display via an online platform, supporting online advertising with real-time performance-based ad ranking and recommendation.

16 . The system of claim 15 , wherein:

the supply data is associated with a user and context information related to a display ad opportunity associated with the ad auction; and

the demand data is associated with the plurality of candidate ads and includes information characterizing each of the plurality of candidate ads.

17 . The system of claim 15 , wherein the determining the diagonal vector d comprises:

accessing the supply data and the demand data;

determining a first matrix U and a transpose matrix UT thereof;

obtaining a vector e and a corresponding square diagonal matrix diag(e); and

computing a diagonal vector d based on UT diag(e) U, wherein

U and e are learned via machine learning based on training data.

18 . The system of claim 15 , wherein the computing the predicted performance metric for the candidate ad via low rank field weighted factorization machines comprises:

extracting the supply data from the ad auction related data;

based on the extracted supply data,

computing a first supply related matrix US, and

computing a second supply related matrix VS;

extracting the demand data from the ad auction related data; and

based on the extracted demand data,

computing a first demand related matrix UD, and

computing a second demand related matrix VD.

19 . The system of claim 18 , wherein the low rank FwFM predicted performance metric determiner is further configured for comprising computing a P matrix based on the first supply related matrix US, the second supply related matrix VS, the first demand related matrix UD, and the second demand related matrix VD.

20 . The system of claim 19 , wherein the low rank FwFM predicted performance metric determiner is further configured for computing, for the candidate ad, the predicted performance metric based on the P matrix and the diagonal vector d.