# Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph
## 3 Approach of This Paper
### Acquisition of Temporal Information  
Temporal information not only reveals the changes in the influence of papers over time but also reflects the dynamic evolution of user interests [22]. The dataset used in this paper provides temporal information, such as the publication date of each paper and the citation dates, which form the foundation for analyzing the research directions and interest trajectories of users. With these data, a time series can be constructed that reflects the shifts in users' research focus and the changes in the influence of papers. Regarding the temporal information of papers, this paper selects the \"citations in Year\", \"cumulative Citations\", and \"citation growth rate\" as features because they offer intuitive indicators of the novelty, research trends, and academic value of the papers. Assuming there is a paper with the following example of temporal information, Table 1 below illustrates the yearly data from the year of publication of 2015 to 2020. After obtaining the features, to enhance the training efficiency and performance of the model, it is common to perform standardization on numerical features. These standardization steps ensure that the feature values are on a unified scale, which helps the model better capture the relationships between features. In this paper, the \"Citations in Year\" and \"Cumulative Citations\" were subjected to min-max scaling, which scales the feature values to a specified range, typically [0, 1]. For the \"Citation Growth Rate\", Z-score standardization was used, which transforms the feature values into a distribution with a mean of 0 and a standard deviation of 1. After the standardization of the data, the feature values are converted into vectors. For example, for the data of the sample paper in Table 1 for the year 2017, their feature vector is \(Feature_{2017}=\) [Citations in Year 2017, Cumulative Citations 2017, Citation Growth Rate 2017]. Assuming that, after standardization, these values are 0.5, 0.3, and 1.5, respectively, then \(Feature_{2017}=\) [0.5, 0.3, 1.5]. Subsequently, Feature2017 is transformed into a feature vector \(FeatureVector_{2017}\) through an embedding layer, and by combining the feature vectors for each year into a sequence, a comprehensive temporal representation for each paper is obtained. For the sample paper, its sequence information is illustrated in Equation (1):  
\[Sequence=[FeatureVector_{2015},FeatureVector_{2016},\ldots,FeatureVector_{2020 }] \tag{1}\]  
For users, this paper transforms the sequence of user-paper interactions (such as publishing, citing, and co-authoring) into a sequence of temporal feature vectors. For each time step \(t\), the user's interaction with papers can be represented as \(j_{t}=[j_{t,1},j_{2},\)\(\ldots,j_{t,n}]\) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specific Time** & **Year** & **Citations in Year** & \
\begin{tabular}{c} **Cumulative** \\ **Citations** \\ \end{tabular} &
\begin{tabular}{c} **Citation Growth** \\ **Rate** \\ \end{tabular} \\ \hline
2015 & 1 & 2 & 2 & / \\
2016 & 2 & 1 & 3 & \(-\)50\% \\
2017 & 3 & 7 & 10 & +600\% \\
2018 & 4 & 5 & 15 & \(-\)28.57\% \\
2019 & 5 & 3 & 18 & \(-\)40\% \\
2020 & 6 & 5 & 23 & +66.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of paper timing information. where \(j_{t}\) is the \(i\)-th feature in the feature vector at time step \(t\), transformed into a feature vector sequence through an embedding layer. A bidirectional recurrent neural network (Bi-RNN) [23] is then utilized to process these feature vector sequences. The forward propagation of the Bi-RNN helps understand users' historical behavior patterns, while the backward propagation helps grasp changes in users' interests. This capability is crucial for understanding long-term dependencies and contextual information within the sequence. In academic paper recommendation systems, the Bi-RNN can assist the model in comprehending the evolution of user behavior.