Embodiments of the present disclosure provide a method and apparatus for document recommendation by obtaining a plurality of first data for a source recommendation task from activities related to a source user using a plurality of first documents; obtaining a plurality of second data for a target recommendation task from activities related to a target user using a plurality of second documents; performing the target recommendation task based on the plurality of first data, the plurality of second data, and knowledge transferred from the source recommendation task to obtain a target recommendation model; and conducting document recommendation to the target user using the target recommendation model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for document recommendation, comprising: obtaining a plurality of first data for a source recommendation task from activities related to a source user using a plurality of first documents; obtaining a plurality of second data for a target recommendation task from activities related to a target user using a plurality of second documents; performing the target recommendation task based on the plurality of first data, the plurality of second data, and information transferred from the source recommendation task to obtain a target recommendation model; and conducting document recommendation to the target user using the target recommendation model. 2. The method according to claim 1, wherein the transferred information comprises subspace importance of the plurality of first documents. 3. The method according to claim 2, wherein: the source recommendation task comprises training a source prediction function (ƒS) based on a source prediction vector (wS) to obtain a source recommendation model; and the target recommendation task comprises training a target prediction function (ƒT) based on a target prediction vector (wT) to obtain the target recommendation model. 4. The method according to claim 3, wherein performing the target recommendation task to obtain the target recommendation model further comprises: dividing respective data in the plurality of first data and the plurality of second data into a plurality of subspaces; obtaining a first optimization element that is a sum of differences between a plurality of prediction values obtained by performing recommendation prediction on the plurality of first documents using the source prediction vector and corresponding actual recommendation values, respectively; obtaining a second optimization element that is a sum of differences between a plurality of prediction values obtained by performing recommendation prediction on the plurality of second documents using the target prediction vector and corresponding actual recommendation values, respectively; obtaining a regularized element that is an element obtained by regularizing the source prediction vector or the target prediction vector under a condition that a modulus value of a sub-vector of the source prediction vector for each of the subspaces is equal to the modulus value of a sub-vector of the target prediction vector for that subspace; and optimizing a sum of the first optimization element, the second optimization element, and the regularized element to obtain a value of the target vector corresponding to the scenario wherein the sum is minimal as a parameter for the target recommendation model. 5. The method according to claim 4, further comprising: generating a plurality of first eigenvectors (xSi) based on the plurality of first data; generating a plurality of second eigenvectors (xTi) based on the plurality of second data; and optimizing the expression: min w S , w T , a   ∑ i = 1 n S   max  ( 0 , 1 - y S i  ( w S  x S i ) ) + ∑ i = 1 n T   max  ( 0 , 1 - y T i  ( w T  x T i ) ) + λ 2  ∑ k = 1 c   ( a k ) 2  s . t .  ∑ j = 1 n k   ( w j , S C = k ) 2 = ∑ j = 1 n k   ( w j , T C = k ) 2 = ( a k ) 2 ,   k = 1 , 2 , …  , c wherein wS and wT denote the prediction vector for the source task and the prediction vector for the target task; nS denotes the number of the plurality of first eigenvectors; xSi denotes the ith first eigenvector; ySi denotes an actual recommendation value for the ith document; nT denotes the number of the plurality of second eigenvectors; xTi denotes the ith second eigenvector; yTi denotes the actual recommendation value for the ith second document; λ is a constant; c denotes the number of the subspaces; nk denotes the number of features in a portion corresponding to the kth subspace in the first eigenvectors, which is identical to the number of features in a portion corresponding to the kth subspace in the second eigen vectors. 6. The method according claim 5, wherein the source prediction function and the target prediction function are support vector machine linear classifiers. 7. An apparatus for document recommendation, comprising: an obtaining module configured to obtain a plurality of first data for a source recommendation task from activities related to a source user using a plurality of first documents, and to obtain a plurality of second data for a target recommendation task from activities related to a target user using a plurality of second documents; a processing module configured to perform the target recommendation task based on the plurality of first data, the plurality of second data, and information transferred from the source recommendation task to obtain a target recommendation model; and an outputting module configured to conduct document recommendation to the target user using the target recommendation model, and output a document recommendation result. 8. The apparatus according to claim 7, wherein the transferred information comprises subspace importance of the plurality of first documents. 9. The apparatus according to claim 8, wherein: the source recommendation task comprises training a source prediction function (ƒS) based on a source prediction vector (wS) to obtain a source recommendation model; and the target recommendation task comprises training a target prediction function (ƒT) based on a target prediction vector (wT) to obtain the target recommendation model. 10. The apparatus according to claim 9, wherein the processing module is further configured to: divide respective data in the plurality of first data and the plurality of second data into a plurality of subspaces; obtain a first optimization element that is a sum of differences between a plurality of prediction values obtained by performing recommendation prediction on the plurality of first documents using the source prediction vector and corresponding actual recommendation values, respectively; obtain a second optimization element that is a sum of differences between a plurality of prediction values obtained by performing recommendation prediction on the plurality of second documents using the target prediction vector and corresponding actual recommendation values, respectively; obtain a regularized element that is an element obtained by regularizing the source prediction vector or the target prediction vector under a condition that a modulus value of a sub-vector of the source prediction vector for each of the subspace is equal to the modulus value of a sub-vector of the target prediction vector for that subspace; and optimize a sum of the first optimization element, the second optimization element, and the regularized element to obtain a value of the target vector corresponding to the scenario wherein the sum is minimal as a parameter for the target recommendation model. 11. The apparatus according to claim 10, wherein the processing module is further configured to: generate a plurality of first eigen vectors (xSi) based on the plurality of first data; generate a plurality of second eigen vectors (xTi) based on the plurality of second data; and optimize the expression: min w S , w T , a   ∑ i = 1 n S   max  ( 0 , 1 - y S i  ( w S  x S i ) ) + ∑ i = 1 n T   max  ( 0 , 1 - y T i  ( w T  x T i ) ) + λ 2  ∑ k = 1 c   ( a k ) 2  s . t .  ∑ j = 1 n k   ( w j , S C = k ) 2 = ∑ j = 1 n k   ( w j , T C = k ) 2 = ( a k ) 2 ,   k = 1 , 2 , …  , c wherein wS and wT denote the prediction vector for the source task and the prediction vector for the target task; nS denotes the number of the plurality of first eigenvectors; xSi denotes the ith first eigenvector; ySi denotes an actual recommendation value for the ith document; nT denotes the number of the plurality of second eigenvectors; xTi denotes the ith second eigenvector; yTi denotes the actual recommendation value of the ith second document; λ is a constant; c denotes the number of the subspaces; nk denotes the number of features in a portion corresponding to the kth subspace in the first eigenvectors, which is identical to the number of features in a portion corresponding to the kth subspace in the second eigenvectors. 12. The apparatus according to claim 11, wherein the source prediction function and the target prediction function are support vector machine linear classifiers. 13. The apparatus according to claim 8, wherein the obtaining module, the processing module and the outputting module may comprise at least one of a hardware element and a software application and a combination thereof.