## 3 Materials and Methods  
In order to make effective recommendations to users, recommendation systems need to solve two problems. One is to predict consumers' product ratings, that is, recommending products with higher predicted scores to target consumers. The second is the interpretation of the recommendation results, that is, explaining the working mechanism of the recommendation system and the specific reasons for recommending a product to consumers in an appropriate way. Since the recommendation process is still a relatively mysterious process for most consumers, a reasonable explanation of the recommendation results is necessary to improve consumers' trust in the recommendation system, which greatly affects consumers' perception and acceptance of recommendation results. The existing recommendation algorithms generally directly rely on the users' overall rating score for products, and the obtained recommendation results are greatly affected by the sparsity of the rating matrix and the cold start problem. This study believes that this situation is mainly caused by the coarse information granularity of the user's product ratings. That is to say, it is impossible for any product to fully meet all the needs of users, and it is impossible for users to have the same degree of preference for all attributes of a product. The recommendation results generated by directly relying on the user's overall ratings cannot reflect the users' preferences for various attributes of the product, and it is difficult to explain the real reasons for the user's preference for the product.  
As consumers are more likely to submit online search phrases to search engines to gather information before making an intended purchase decision. They enter keywords to explicitly express their preferences for product attributes. For example, customers formulate queries such as \"best laptop for programming\" that directly reflect their content preferences for product configurations. Interpreting consumers' search phrases renders a better understanding of their purchase intentions and preferences for product attributes, which is critical for developing an effective personalized recommendation system.  
In this paper, we introduce a sustainable recommender system architecture based on fusing a topic model and a hypergraph neural network, which can deal with the interaction behavior of consumers in the whole process of shopping (i.e., searching-understanding-purchasing-using). Figure 1 shows the topic augmented hypergraph neural network (Topic-HGNN) framework for searching-scenario oriented recommendation. 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 the interaction graph domain. Then, we utilize hyperedge corruption [47] to generate a user-query hypergraph and an item-feature hypergraph, and we utilize the hyperedge convolution layer [46] 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 to 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.