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AI & Systems
Prompt Description
Based on the research framework of the National Natural Science Foundation of China, the research topic is data augmentation technology for implicit feedback recommendation. The three research contents are: (1) adaptive data generation and filtering methods based on large models; (2) noise data mining and correction methods under the collaborative paradigm; and (3) causal debiasing methods in multi-behavior recommendation. The three key problems are: (1) optimization of generated data quality; (2) the identification mechanism of noise data; and (3) causal relationship modeling between multiple behaviors. The research goal is to build a robust recommendation system. Please draw the research framework diagram.
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![Please generate a clear block diagram illustrating the architecture of the following neural network model: Model Name: SAT-MoE. The left side has two input blocks: 1) Process features `opes` with shape [batch, num_opes, in_size_ope]; 2) Machine features `mas` with shape [batch, num_mas, in_size_ma]. First, apply two linear projections to obtain hidden representations of the same dimension, then add type embeddings (process type = 0, machine type = 1), and additionally add positional encoding to the process sequence. Subsequently, apply LayerNorm to both paths separately, and then concatenate them along the sequence dimension into a long sequence, which is fed into a stacked Transformer Encoder (self-attention, multi-head attention, feed-forward network). A Mixture-of-Experts (MoE) module can be connected in the middle: a gating network outputs weights for multiple MLP experts, and the expert outputs for each token are weighted and summed, followed by a residual connection and LayerNorm. The Transformer output is then split back into two paths according to the original number of processes and machines: process representation and machine representation. Then, pass them through two cross-attention layers sequentially: The first "Process → Machine" attention: process features serve as Query, and machine features serve as Key/Value, to obtain the updated process representation; The second "Machine → Process" attention: machine features serve as Query, and process features serve as Key/Value, to obtain the updated machine representation. The final outputs are the updated process features `out_opes` and machine features `out_mas`. Please use a left-to-right data flow layout, connect the modules with arrows, label the name of each module (such as Input Projection, Type Embedding, Positional Encoding, Transformer Encoder, MoE, Ope-to-Machine Attention, Machine-to-Ope Attention, etc.), and make the diagram style concise and suitable for paper presentation.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2FQrRrIgUwJnufqN4Havc2b4pFYV84PSTW%2F763c7d61-fe20-46a0-af4e-ddec53f57135%2F26fdf95b-f6f5-47a1-bf67-976f398ab221.png&w=3840&q=75)
![The core of each iteration lies in generating offspring individuals that satisfy constraints and completing the fitness evaluation of the entire population. This provides a complete basis for selecting the new generation of the population. This stage is divided into three sub-steps: generating crossover offspring, generating adaptively mutated offspring, and population merging and batch evaluation. In crossover offspring generation, the offspring size is set to 10,000 to ensure the efficiency of population iteration and update. The tournament selection strategy is used for parent selection, selecting two parent individuals with better fitness from the current population to improve the overall quality of the offspring. At the same time, the crossover logic must meet constraint conditions. The specific steps are to separate the absolute value and sign of the parent parameters to avoid destroying the constraint conditions during the crossover process; perform linear interpolation crossover on the absolute values (α∈[0,1] is randomly selected) to generate the absolute value of the offspring parameters; perform non-negative clipping on the crossed absolute values, and then normalize them to ensure that the sum of the absolute values is 1; randomly inherit the parent parameter signs and reconstruct the complete offspring parameter vector. In adaptive mutation offspring generation, the mutation offspring size is set to 1,000 to balance population diversity and computational efficiency. The parent is selected from the current population through tournament selection to select a high-quality parent individual and clone it as a mutation template. At the same time, the mutation intensity dynamically decays from 0.05 to 0.005 with the number of iterations, taking into account early exploration and late refinement. Non-negative clipping and normalization are performed on the mutated absolute values to ensure that the constraint conditions are met. Finally, the parameter sign is randomly flipped with a probability of 10% to further enhance the population's exploration ability. In population merging and batch evaluation, the current population (10,000), crossover offspring (10,000), and mutation offspring (1,000) are merged into a temporary full population (21,000). Individuals with invalid fitness in the temporary population are extracted, and batch weighted comprehensive error calculation is performed on the invalid individuals, and assigned as fitness values to complete the fitness update of the full population. The minimum fitness (minimum error) and average fitness (average error) of the current generation are recorded to provide data support for iteration monitoring.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2FXncgWQfEIJ7VYt2PJn6a3h2aOyb9nOUf%2F77e1142f-8028-491e-905e-fa27ec6c0e30%2Fb70b81d0-4503-4e08-983f-a0d0c89ad3e8.png&w=3840&q=75)