## 3 The ALIEN Architecture
### Main Idea
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(\mathcal{U},\mathcal{V}\) & the sets of users and items \\ \(\mathbf{u}\in\mathbb{R}^{m\times k},\mathbf{v}\in\mathbb{R}^{n\times k}\) & user \(u\)’s and \(v\)’s \(k\)-dimensional embeddings \\ \(\mathbf{S}^{v_{i}}_{u}\) & the embedding set of \(u\)’s history items related to \(u\)’s candidate item \(v_{c}\) \\ \(\mathbf{\widetilde{u}}^{\prime},\mathbf{\widetilde{v}}^{\prime}\) & \(u\)’s and \(v\)’s high-dimensional and sparse one-hot encodings which contain all the fields of attributes \\ \(\mathbf{W}_{\widetilde{u}^{(i)}}\in\mathbb{R}^{k\times|\widetilde{\mathbf{u}}^{(i)}|}, \mathbf{W}_{\widetilde{v}^{(i)}}\in\mathbb{R}^{k\times|\widetilde{\mathbf{v}}^{(i)}|}\) & the weights assigned to \(\mathbf{\widetilde{u}}^{(i)}\) and \(\mathbf{\widetilde{v}}^{(i)}\) for latent space projection \\ \(\ell_{\mathcal{F}^{\mu\times r}_{(i)}},\mathcal{F}^{\mu\times r}\) & the numbers of feature interactions of the \(i\)-order and all the orders \\ \(\mathbf{\mathcal{F}}^{\mu\times r}\in\mathbb{R}^{\ell_{\mathcal{F}^{\mu\times r}} \times k}\) & the complete representation vector of feature interactions of all the orders \\ \(\mathbf{\widetilde{\mathcal{F}}}^{\mu\times r}\in\mathbb{R}^{\ell_{\mathcal{F}^{\mu \times r}}\times k}\) & the output vector of the Synthesizer module \\ \(\mathbf{X}^{l}_{h}\) & the \(h\)-th feature vector of the \(l\)-th layer in the CIN module \\ \(\mathbf{a}_{syn}\) & the feature interaction-level attention score matrix for \(\mathbf{\mathcal{F}}^{\mu\times r}_{(1)}\) \\ \(\psi_{l}(\cdot)\in\mathbb{R}^{\ell_{\mathcal{F}^{\mu\times r}}\times\ell_{ \mathcal{F}^{\mu\times r}}}_{(1)}\) & the Multi-Layer Perceptron units in the Synthesizer module \\ \(\sigma(\cdot)\) & the Dice activation function \\ \(\mathbf{W}_{syn}\in\mathbb{R}^{\ell_{\mathcal{F}^{\mu\times r}}\times\ell_{ \mathcal{F}^{\mu\times r}}}_{(1)}\) & the randomly initialized matrix utilized in the Synthesizer module \\ \(\mathbf{\mathcal{I}}^{v_{c}}_{u}\) & the intensity of \(u\)’s interest in \(v_{c}\) \\ \(\mathbf{w}_{ci}\) & the correlation score between \(\mathbf{v}_{c}\) and \(\mathbf{v}_{i}\) \\ \(\mathbf{\mathcal{P}}^{\mu}_{u}\) & \(u\)’s preference vector for \(v_{c}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation descriptions. implies \(u\)'s historical interests, but also provides clues as to why \(u\) will choose to click the candidate item \(v_{c}\) (or not). Given \(v_{c}\), a user's interest related to \(v_{c}\) can be locally activated, and therefore its intensity is available for measurement, which is regarded as one important factor to improve the CTR prediction in our work. Latent information digestion and prediction layer (PREDICTION): This layer concatenates the outputs of the first two layers and feeds them into a fully connected layer with the Dice activation function to generate the CTR prediction of \(v_{c}\) for \(u\). Dice standardizes the input data and puts the mean at the inflection point of the sigmoid, which is important when the inputs of each layer follow different distributions. The detailed work flow of these three layers is discussed in Section 3.4. The main idea of our approach is to:  
1. Build a basic user profile by constructing the low and high-order feature interactions from user and item attributes, and assign them different weights, to automatically learn the different influences on the candidate item. 2. Enrich the user's profile comprehensively by locally activating \(u\)'s interest related to \(v_{c}\) with \(u\)'s corresponding history items \(S_{u}^{v_{c}}\), and measure the intensity of \(u\)'s locally activated interest, to enhance the performance of CTR prediction. Figure 1: ALIEN architecture.