# Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph
## 3 Approach of This Paper
### Acquisition of Temporal Information
For instance, a user's research focus may gradually develop and change over time, or their interest in a particular research topic may intensify or diminish due to the latest academic developments. The advantage of the bidirectional recurrent neural network (Bi-RNN) lies in its capability to process sequential data, analyzing both historical user behavior and predicting future trends simultaneously. This makes Bi-RNN particularly adept at capturing the evolution of user interests, providing strong support for delivering accurate academic paper recommendations. The size and number of hidden layers affect the Bi-RNN model's complexity and capacity, while the learning rate and choice of optimizer impact the training dynamics and convergence speed. Considering experimental outcomes and time costs, the hidden layer size is set to 256 with two layers, a learning rate of 0.001, and the Adam optimizer is selected. The hidden state \(h_{t}\) of the Bi-RNN can be obtained through Equation (2), where \(h_{t-1}\) and \(h_{t+1}\) are the hidden states at the preceding and succeeding time points, respectively:  
\[h_{t}=\text{Bi-RNN}(j_{t},h_{t-1},h_{t+1}) \tag{2}\]  
To highlight the contributions of significant time points in the sequence and to capture the user's recent interests, an attention score \(a_{t}\) is first defined for each time step \(t\). This score reflects the relative importance of the interaction at that time point compared to others. The attention score can be obtained through Equation (3):  
\[a_{t}=\text{softmax}(\frac{\exp(v^{\text{T}}\tanh(W_{a}h_{t}+b_{a}))}{\sum_{k }\exp(v^{\text{T}}\tanh(W_{a}h_{k}+b_{a}))}) \tag{3}\]  
where \(v\) is the weight vector used to calculate the attention weights, \(W_{a}\) and \(b_{a}\) are learnable parameters, and \(h_{t}\) is the hidden state vector at time point \(t\). Next, a time-sensitive weight \(w_{t}\) is introduced to adjust the attention scores for each time point, allowing the model to place greater emphasis on recent interactions, that is, the user's most current points of interest. The time-sensitive weight can be obtained through Equation (4):  
\[w_{t}=\lambda e^{-a(T-t)} \tag{4}\]  
In the aforementioned equation, \(\lambda\) is a positive constant used to adjust the magnitude of the weights, \(\alpha\) is a hyperparameter that controls the rate of temporal decay, and \(T\) represents the total number of time points in the sequence. Ultimately, the user's temporal sequence vector \(S_{u}\) is computed using the weighted attention scores and time-sensitive weights, as shown in Equation (5). The symbol \(\odot\) in the equation denotes element-wise multiplication:  
\[S_{u}=\sum_{t}h_{t}\odot a_{t}\odot w_{t} \tag{5}\]  
Through this approach, the model can learn the importance of each interaction and place greater emphasis on the user's recent interests. The method that combines the attention mechanism with time-sensitive weights allows the model to focus more on the user's short-term interests while retaining the contextual information of long-term interactions.