## 1 Introduction
Another hybrid recommendation mechanism is utilizing multiple independent recommendation algorithms, each of which generates its own recommendation results, and fusing these recommendation results in the mixing stage to generate the final recommendation result. It can be seen from the above analysis that the above recommendation techniques predict consumer purchase intention based on product ratings of what other similar users have purchased or what they themselves have purchased. User preferences characterized by these methods are commonly presented based on user ratings of 1 to 5, which can capture a user's overall evaluation of the product. However, ratings data is too simple to capture consumers' multi-dimensional and fine-grained evaluation of product attributes. Unlike sparse consumer purchasing data, consumers conduct extensive online search queries before making a purchasing decision. Take the laptop as an example, customers formulate queries like \"best laptop for programming\" that directly reflect their content preferences for product features. Thus, it is important to understand the navigation keywords associated with product features in users' online query sessions. It is critical for e-commerce platforms to extract consumer content preferences from online search sessions [14]. Despite the importance of inferring user preferences from online query sessions, few studies have focused on this area. [PERSON] et al. [15] revealed that online search queries focused on superficial product features rather than key knowledge. Information search behavior is an important factor that is assessed to identify differences in consumers regarding their purchasing patterns and preferences [16]. [PERSON] et al. [17] verified that there is a significant relationship between new product diffusion and internet search volume. Internet search volume is an important indicator for predicting new product demand. [PERSON] and [PERSON] [18] suggested that marketers should focus their efforts on keywords and queries that reflect content preferences that are well aligned with the content they are trying to promote. [PERSON] et al. [19] found that these browsing data can be saved with cookies and can be used to show customers potentially suitable items. Although numerous studies have been conducted to empirically verify that online queries can explicitly express consumers' content preference or can be used to predict product demand, quantitative studies that can estimate content preferences from online queries in an interpretable manner are lacking. Therefore, it is managerially important for sustainable e-retailers to develop intelligent recommendations based on learning dynamic customer preference from online query sessions [20]. In this study, we develop a topic augmented hypergraph neural network (Topic-HGNN) framework, which uses the hypergraph structure to capture the complex multivariate relationship among users, query topics, items, and item features. Besides, we incorporate topic models into the hypergraph neural network to more finely depict user preferences and product characteristics. To this end, we specifically propose an Aggregated Latent Dirichlet Allocation model to jointly extract users' content preference topics from queries and webpages, and apply the Latent Dirichlet Allocation [21] model to extract product feature topics from online reviews, which is useful to enhance feature interaction interpretability.