A deep collaborative filtering (DCF) approach is employed in a recommender system to provide item recommendations to users. The DCF approach combines deep learning models with matrix factorization based collaborative filtering. To provide item recommendations, a user-item rating matrix, user side information, and item side information are provided as input to a recommender system. The recommender system learns user latent factors and item latent factors by jointly: (1) decomposing the user-item rating matrix to extract latent factors, and (2) extracting latent factors from hidden layers of deep learning models using the user side information and item side information. The learned user latent factors and item latent factors are used to predict item ratings for missing ratings in the user-item rating matrix. The predicted item ratings are then used to select item recommendations for a given user, which are then communicated to a user device of the user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for providing item recommendations to a user device of a user, the method comprising: receiving a user-item rating matrix, user side information, and item side information; generating, using a processor, learned user latent factors and learned item latent factors by jointly: (1) decomposing the user-item rating matrix to extract latent factors from the user-item rating matrix, and (2) extracting latent factors from hidden layers of deep learning models using the user side information and item side information as input layers to the deep learning models; generating predicted item ratings for at least a portion of the missing ratings in the user-item rating matrix using the learned user latent factors and learned item latent factors; selecting one or more item recommendations for the user based on at least a portion of the predicted item ratings; and communicating, via one or more communication networks, the one or more item recommendations to the user device associated with the user. 2. The computer-implemented method of claim 1, wherein the user-item rating matrix is decomposed using probabilistic matrix factorization, and wherein the deep learning models comprise marginalized denoising autoencoders. 3. The computer-implemented method of claim 1, wherein the user-item rating matrix comprises item ratings, each item rating comprising a rating on a rating scale provided by a given user for a given item, and wherein each predicted item rating comprises a predicted rating on the rating scale. 4. The computer-implemented method of claim 1, wherein the user-item rating matrix comprises item ratings, each item rating indicating whether a given user performed an action for a given item, and wherein each predicted item rating comprises a response prediction. 5. The computer-implemented method of claim 1, wherein jointly decomposing the user-item rating matrix to extract latent factors from the user-item matrix and extracting latent factors from the hidden layers of the deep learning models using the user side information and item side information as input layers comprises: initializing the user latent factors, the item latent factors, a user projection matrix, and an item projection matrix; repeating until convergence: updating a user mapping function as a function of the user side information, the user latent factors, and the user projection matrix, updating an item mapping function as a function of the item side information, the item latent factors, and the item projection matrix, updating the user projection matrix as a function of the user side information, the user mapping function, and the user latent factors, updating the item projection matrix as a function of the item side information, the item mapping function, and the item latent factors, updating the user latent factors as a function of the user side information, the user projection matrix, and the user mapping function, and updating the item latent factors as a function of the item side information, the item projection matrix, and the item mapping function; and upon convergence, outputting the user latent factors and the item latent factors. 6. The computer-implemented method of claim 5, wherein a plurality of layers are employed, wherein the updating steps are performed for a └(l+1)/2]┘ layer in which l represents the number of layers, and wherein only the user mapping function and item mapping function are updated for all other layers. 7. One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations for predicting item ratings for users, the operations comprising: receiving as input: a user-item rating matrix, user side information, and item side information; initializing user latent factors, item latent factors, a user projection matrix, and an item projection matrix; repeating until convergence: updating a user mapping function as a function of the user side information, the user latent factors, and the user projection matrix, updating an item mapping function as a function of the item side information, the item latent factors, and the item projection matrix, updating the user projection matrix as a function of the user side information, the user mapping function, and the user latent factors, updating the item projection matrix as a function of the item side information, the item mapping function, and the item latent factors, updating the user latent factors as a function of the user side information, the user projection matrix, and the user mapping function, and updating the item latent factors as a function of the item side information, the item projection matrix, and the item mapping function; upon convergence, outputting learned user latent factors and learned item latent factors; and generating predicted item ratings for missing entries in the user-item rating matrix based on the learned user latent factors and learned item latent factors. 8. The one or more computer storage media of claim 7, wherein the user mapping function is updated according to the following equation: W1=E[S1]E[Q1]−1, wherein W1 represents the user mapping function, S1=X{tilde over (X)}T+λP1UTXT, and Q1=X{tilde over (X)}T+λXXT, and wherein X represents the user side information, {tilde over (X)} represents a c-times repeated version of X, {tilde over (X)} represents a corrupted version of X, λ represents a trade-off parameter, and P1 represents the user projection matrix. 9. The one or more computer storage media of claim 7, wherein the item mapping function is updated according to the following equation: W2=E[S2]E[Q2]−1, wherein W2 represents the item mapping function, S2=Y{tilde over (Y)}T+λP2VTYT and Q1=Y{tilde over (Y)}+λYYT, and wherein Y represents the item side information, Y represents a c-times repeated version of Y, {tilde over (Y)} represents a corrupted version of {tilde over (Y)}, λ represents a trade-off parameter, and P2 represents the item projection matrix. 10. The one or more computer storage media of claim 7, wherein the user projection matrix is updated according to the following equation: P1=W1XU(UTU)−1, wherein P1 represents the user projection matrix, W1 represents the user mapping function, X represents the user side information, and U represents the user latent factors. 11. The one or more computer storage media of claim 7, wherein the user projection matrix is updated according to the following equation: P2=W2YV(VTV)−1, wherein P2 represents the item projection matrix, W2 represents the item mapping function, Y represents the item side information, and V represents the item latent factors. 12. The one or more computer storage media of claim 7, wherein the user latent factors and item latent factors are updated using the following update rules: u i = u i - γ  ∂ ∂ u i  f  ( U , V ) , v j = v j - γ  ∂ ∂ v j  f  ( U , V ) wherein γ is a learning rate, and wherein: ∂ f  ( U , V ) ∂ u i = λ  ( P 1 ⊤  ( P 1  u i - ( W 1  X ) i ) ) + β   u i - α  ∑ ( i , j ∈  )  ( R i , j - u i  v j ⊤ )  v j . ∂ f  ( U , V ) ∂ v j = λ  ( P 2 ⊤  ( P 2  v j - ( W 2  Y ) j ) ) + β   v j - α  ∑ ( i , j ∈  )  ( R i , j - u i  v j ⊤ )  u i . wherein ui represents user latent factors for a user i, λ, β, and α represents trade-off parameters, P1 represents the user projection matrix, W1 represents the user mapping function, X represents the user side information, vj represents item latent factors for an item j, P2 represents the item projection matrix, W2 represents the item mapping function, Y represents the item side information, and Rij represents an user item rating for a user i and item j. 13. The one or more computer storage media of claim 7, wherein the operations further comprise: selecting one or more item recommendations for a user based on at least a portion of the predicted item ratings; and communicating, via one or more communication networks, the one or more item recommendations to a user device associated with the user. 14. The one or more computer storage media of claim 7, wherein the user-item rating matrix comprises item ratings, each item rating indicating whether a given user performed an action for a given item, and wherein each predicted item rating comprises a response prediction. 15. The one or more computer storage media of claim 7, wherein a plurality of layers are employed, wherein the updating steps are performed for a └(l+1)/2]┘ layer in which l represents the number of layers, and wherein only the user mapping function and item mapping function are updated for all other layers. 16. A computerized system for providing item recommendations to a user device of a user, the system comprising: one or more processors; and one or more computer storage media comprising computer-useable instructions for causing the one or more processors to perform operations, the operations comprising: receiving a user-item rating matrix, user side information, and item side information; generating learned user latent factors and learned item latent factors by jointly: (1) decomposing the user-item rating matrix to extract latent factors from the user-item rating matrix, and (2) extracting latent factors from hidden layers of deep learning models using the user side information and item side information as input layers to the deep learning models; generating predicted item ratings for missing ratings in the user-item rating matrix using the learned user latent factors and learned item latent factors; selecting one or more item recommendations for the user based on at least a portion of the predicted item ratings; and communicating, via one or more communication networks, the one or more item recommendations to the user device associated with the user. 17. The computerized system of claim 16, wherein the user-item rating matrix is decomposed using probabilistic matrix factorization, and wherein the deep learning models comprise marginalized denoising autoencoders. 18. The computerized system of claim 16, wherein the user-item rating matrix comprises item ratings, each item rating indicating whether a given user performed an action for a given item, and wherein each predicted item rating comprises a response prediction. 19. The computerized system of claim 16, wherein jointly decomposing the user-item rating matrix to extract latent factors from the user-item rating matrix and extracting latent factors from the hidden layers of the deep learning models using the user side information and item side information as input layers comprises: initializing the user latent factors, the item latent factors, a user projection matrix, and an item projection matrix; repeating until convergence: updating a user mapping function as a function of the user side information, the user latent factors, and the user projection matrix, updating an item mapping function as a function of the item side information, the item latent factors, and the item projection matrix, updating the user projection matrix as a function of the user side information, the user mapping function, and the user latent factors, updating the item projection matrix as a function of the item side information, the item mapping function, and the item latent factors, updating the user latent factors as a function of the user side information, the user projection matrix, and the user mapping function, and updating the item latent factors as a function of the item side information, the item projection matrix, and the item mapping function; and upon convergence, outputting the user latent factors and the item latent factors. 20. The computerized system of claim 19, wherein a plurality of layers are employed, wherein the updating steps are performed for a └(l+1)/2]┘ layer in which ; represents the number of layers, and wherein only the user mapping function and item mapping function are updated for all other layers.