## 5 Conclusions  
Personalized product recommendation systems are a useful tool adopted by e-retailers to help consumers find items in line with their preferences. Existing research focuses on learning user interaction characteristics with products during and after purchase (e.g., purchase and online review), while ignoring the interactive features in the process of users' learning about product information through search queries before they make a purchase (e.g., product information search). However, users' association with a product is a coherent process that should not be isolated into different nodes. Only sorting the user's process of searching-understanding-purchasing-using products and finding opportunity points from each stage can help the recommender system to better discover the potential needs of users. To this end, we develop a topic augmented hypergraph neural network framework to predict users' purchase intentions by connecting the latent topics embedded in consumers' online queries to their click, purchase, and online review behavior. First, we adopt a hypergraph to model the multivariate relationship among users, query topics, items, and item features, which aims at mining the connection information existing in \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Memory Consumption (103 Queries)** & **Memory Consumption (104 Queries)** & **Memory Consumption (105 Queries)** \\ \hline CTR & 83 MB & 347 MB & 970 MB \\ \hline SVD-LDA & 89 MB & 385 MB & 1102 MB \\ \hline CoAWILDA & 96 MB & 403 MB & 1165 MB \\ \hline AR-LDA & 77 MB & 311 MB & 928 MB \\ \hline EUU-CF & 79 MB & 284 MB & 944 MB \\ \hline Graph-CNN & 882 MB & 1509 MB & 3259 MB \\ \hline Hyper-CTR & 926 MB & 1647 MB & 3895 MB \\ \hline Topic-HGNN & 974 MB & 1802 MB & 3971 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The memory consumption of each method under different query search volumes on Retailrock dataset (the number of recommendation results is 10). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Running Time** (103 Queries)** & **Running Time (104 Queries)** & **Running Time (105 Queries)** \\ \hline CTR & 12.12 ms & 2095.54 ms & 49,514.16 ms \\ \hline SVD-LDA & 11.87 ms & 2294.63 ms & 48,510.53 ms \\ \hline CoAWILDA & 11.65 ms & 3220.22 ms & 46,767.78 ms \\ \hline AR-LDA & 8.02 ms & 3076.21 ms & 38,881.03 ms \\ \hline EUU-CF & 8.54 ms & 3085.96 ms & 48,736.47 ms \\ \hline Graph-CNN & 15.57 ms & 5014.59 ms & 58,294.41 ms \\ \hline Hyper-CTR & 19.56 ms & 4963.22 ms & 59,324.57 ms \\ \hline Topic-HGNN & 19.67 ms & 5038.40 ms & 58,290.89 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The running time of each method under different query search volumes on Retailrock dataset (the number of recommendation results is 10). the interaction graph domain. Then, we utilize the hyperedge corruption to generate a user-query hypergraph and an item-feature hypergraph and utilize the hyperedge convolution layer to obtain user embedding and item embedding. Meanwhile, in order to reduce the influence of text noise words by fusing topic information, we specially design an Aggregated Latent Dirichlet Allocation (ALDA) model to jointly extract users' content preference topics from queries and webpages and apply Latent Dirichlet Allocation model to extract product feature topics from online reviews. Then, we integrate the topic distribution and convolutional embedding to represent each user and item, which can make up for the lack of topic information in traditional convolutional neural networks. Finally, we use multilayer perceptron to calculate the soft match score between query entities and item entities. Extensive empirical evaluations on real-world datasets demonstrate that the proposed framework could improve the novelty of recommendation items without sacrificing accuracy.