Knowledge Graph-Enhanced Recommendation with Box Embeddings (2024)

Abstract: “Knowledge graphs are used to alleviate the problems of data sparsity and cold starts in recom-mendation systems. However, most existing approaches ignore the hierarchical structure of theknowledge graph. In this paper, we propose a box embedding method for knowledge graph-enhanced recommendation system. Specifically, the box embedding represents not only the in-teraction between the user and the item, but also the head entity, the tail entity and the relationbetween them in the knowledge graph. Then the interaction between the item and the corre-sponding entity is calculated by the multi-task attention unit. Experimental results show thatour method provides a large improvement over previous models in terms of Area Under Curve(AUC) and accuracy in publicly available recommendation datasets with three different domains.”

## introduction recommendation systems have been proposed to solve the information explosion problem and to improve the user experience in various applications. early recommendation systems were mainly based on collaborative filtering algorithms. these algorithms build user-item interaction matrices and perform similarity calculations to obtain final recommendations. however, collaborative filtering-based approaches encounter data sparsity and cold-start problems in practical applications. to address these problems, some researchers have proposed recommendation methods that use knowledge graphs (kgs) as auxiliary information to improve the performance of recommendation tasks. since kgs contain rich semantic relationships between entities corresponding to user-related items, the recommended items can take advantage of interactions with entities in the kg. previous knowledge graph enhanced recommendation methods mainly used multiple modules to learn the representation of knowledge graph and recommendation task independently. for example, cke @xcite used the transr to encode the knowledge graph, while extracted the text and image features of the recommendation task through another self-encoding structure. recently, several researchers have proposed methods for learning entities and items using multi-task learning. the core idea of such methods trained feature interaction layers jointly to connect the recommendation task and the knowledge graph-related task. for example, mkr @xcite used cross units for training the knowledge graph embedding task and recommendation task. but the units were computed by matrix multiplication. this lead to the insufficient learning about the feature. in addition, few existing methods can model the semantic hierarchical structure relationship between recommendation data and the knowledge graph. to address above shortcomings, we proposed boxmakr, a box embedding for knowledge graphenhanced recommendation. specifically, our model utilizes the attention mechanism to learn higherorder interaction features between items and entities more efficiently, and then embeds semantic hierarchical relationships into entities, users and items. we conduct experiments on three recommendation datasets, and the results show that boxmakr proposed in this paper achieves a large improvement compared with advanced models such as mkr. besides user reviews information, knowledge graphs also can be considered as an additional information source. this kind of task is called knowledge graph enhanced recommendation system. they are usually contain two subtasks: knowledge graph embedding and the recommendation task. to integrate the two tasks more effectively, more and more researchers focused on multi-task learning. for instance, mkr @xcite designed an interaction unit to share the features between items and entities and trained jointly. ripp-mkr @xcite used the core idea of rippnet @xcite to combine the knowledge graph with historical user interaction to represent user features based on mkr. transmkr @xcite improved the knowledge graph embedding module of mkr with transr, which enhanced the representation capability of knowledge graph embedding. ## problem formula in the recommendation task, there exists a user set @xmath0, u 2 , ..., u m }, an item set @xmath1, v 2 , ..., v n }, and a user-item interaction matrix @xmath2, where y @xmath3, 1}. this user-item matrix represents whether an interaction exists between user u m and item v n , which is defined by users' feedbacks. in the matrix y, y @xmath4, otherwise y @xmath5. these feedbacks include implicit users' click, watching, browsing. furthermore, a knowledge graph g can be composed of many triples (h, r, t). where h denotes the head entity, t denotes the tail entity, and r denotes the relationship between them. in the knowledge graph each triple is associated with the itemsv. also, the item @xmath6. thus, the knowledge graph enhanced recommendation task can be formulated as follows: given a user set u , an item set v , a user-item interaction matrix y and a knowledge graph g, the model will predict the ŷuv which means the user u is interested in item v. c c l 2 0 2 4 4 method ## conclusions this paper proposed a box embedding method for knowledge graph enhanced recommendation system. based on the mkr model, we use box embeddings to represent users, items and entities. this embeddings allow us to model the semantic hierarchical structure relationships between those objects. further, the multi-task attention method learned the higher-order interactions between items and corresponding entities. we conduct extensive experiments in three recommendation datasets. the results showed that our method is better than the original model mkr, and have great advantages over other baseline models.