Method and electronic device for recovering data using bi-branch neural network

A computer-implemented method for determining one or more recommendation items from one or more items for one or more user by an electronic device, comprising:

• receiving, by a processor of the electronic device, object data, wherein the object data comprises a matrix, wherein rows of the matrix correspond to user IDs of the users respectively, columns of the matrix correspond to item IDs of items respectively, and each entry of the matrix indicates a rating related to corresponding item ID and corresponding user ID;
• identifying, by the processor, values of first entries (xi,j) of the matrix according to the object data;
• inputting, by the processor, the entries (xi,j) and a preset maximum loop count (Kmax) into an executed analysis model using Bi-Branch Neural Network (BiBNN) algorithm;
• obtaining, by the processor, a plurality of second entries (mi,j) of from the analysis model, wherein values of the second entries are determined as original ratings of the matrix, such that unknown ratings of the part of the first entries are predicted;
• and
• regarding each user ID, selecting, by the processor, one or more item IDs having ratings higher than a rating threshold, so as to determine one or more recommendation items corresponding to the selected item IDs for user corresponding to each user ID.

An electronic device for performing data recovering operation, comprising:

• a processor, configured to execute machine instructions to implement a computer-implemented method, the method comprising:
• receiving, by a processor of the electronic device, object data, wherein the object data comprises an incomplete matrix;
• identifying, by the processor, a plurality of first entries (xi,j) of the incomplete matrix according to the object data,
• inputting, by the processor, the first entries (xi,j) and a preset maximum loop count (Kmax) into an executed analysis model using Bi-Branch Neural Network (BiBNN) algorithm;
• and
• obtaining, by the processor, a plurality of second entries (mi,j) of a recovered complete matrix corresponding to the incomplete matrix from the analysis model, wherein values of the second entries are determined as original values of the first entries of the incomplete matrix, such that incorrect data in the incomplete matrix is recovered.

An electronic device for determining one or more recommendation items for one or more user from one or more items, comprising:

• a processor, configured to execute machine instructions to implement a computer-implemented method, the method comprising:
• receiving, by a processor of the electronic device, object data, wherein the object data comprises a matrix, wherein rows of the matrix correspond to user IDs of the users respectively, columns of the matrix correspond to item IDs of items respectively, and each entry of the matrix indicates a rating related to corresponding item ID and corresponding user ID;
• identifying, by the processor, values of first entries (xi,j) of the matrix according to the object data;
• inputting, by the processor, the entries (xi,j) and a preset maximum loop count (Kmax) into an executed analysis model using Bi-Branch Neural Network (BiBNN) algorithm;
• obtaining, by the processor, a plurality of second entries (mi,j) of from the analysis model, wherein values of the second entries are determined as original ratings of the matrix, such that unknown ratings of the part of the first entries are predicted;
• and
• regarding each user ID, selecting, by the processor, one or more item IDs having ratings higher than a rating threshold, so as to determine one or more recommendation items corresponding to the selected item IDs for user corresponding to each user ID.