# Knowledge Graph Recommendation Model Based on Feature Space Fusion
## 3 Our Model  
### Problem Analysis and Solution  
The item-related content feature contained in the knowledge graph is used to assist the recommendation, which effectively alleviates the questions of sparse data and cold start faced by the collaborative filter recommendation model. However, many current recommendation models based on knowledge graphs combine UIG and KG into CKG and use the graph convolution neural network algorithm to spread information on the CKG. This kind of model simply integrates the behavioral features in the UIG, and the content features in the KG in the same space will ignore that they are actually in different feature spaces and express different semantics. At the same time, the constructed user features and item features are mixed with irrelevant noise information. For example, as shown in Figure 1, The UIG of the green rectangular and the item-related KG of the blue rectangle constitute CKG. In the UIG, users A and B jointly interact with \"The Shawshank Redemption\", and they are users with similar behaviors; \"The Shawshank Redemption\" and \"Titanic\" have both been interacted with by User B, and the two are movies with similar interaction behavior. After analyzing the behavior features of user groups, it is found that users often choose to watch \"Titanic\", which is also an Oscar-winning film, after watching \"The Shawshank Redemption\". Therefore, \"Titanic\" is likely to be a movie that User A will watch through the message propagation path of the red dotted arrow. In the knowledge graph, the movie that User A has interacted with contains the attributes of \"comedy\", \"plot\", and \"crime\" through the blue dotted arrow, indicating that User A is interested in these contents. Similarly, User B is interested in including \"plot,\" \"crime,\" and \"love.\" When the information is disseminated on the CKG, the information of \"love\" and \"tragedy\" is transmitted to \"The Shawshank Redemption\" and User A through the path indicated by the red dotted arrow in the figure. Although \"The Shawshank Redemption\" and \"Titanic\" have been interacted with by User B, the two have no connection in content, and the interaction history of User A does not include movies with content such as \"love\" and \"tragedy\". This raises two problems: (1) for the user, because User A is spread with the message of \"tragedy\", when recommending a movie for User A, the recommendation result will appear in the recommendation result that User A has no interest in \"Romeo and Juliet\"; (2) for the item, because \"The Shawshank Redemption\" is spread with \"love\" and \"tragedy\" information, \"The Shawshank Redemption\" will be recommended to users who like \"love\" or \"tragedy\". In fact, the behavioral feature in the UIG and the content feature in the KG contain different semantic information, and the two describe the features of users and items from different aspects. In Figure 1, User A interacts with \"Forrest Gump\" and \"The Shawshank Redemption\", and the semantic messages behind the interaction include the following: the two movies are in line with User A's preferences; the two movies are popular; User A was recommended by a friend to watch the two movies; and so on. Therefore, the semantic information behind the user's interaction behavior is complex and diverse. In contrast, the content feature contained in the knowledge graph is more specific: \"comedy\", \"plot\", and \"crime\" information can be interpreted as User A's preference for the specific content of the movie. Through comparison, it can be found that the semantic information contained behind the behavioral feature and content feature is unequal, the behavioral feature reflects the complex factors behind the user interaction, and the content feature reflects the specific content that the user likes. Message propagation on CKG ignores the difference in semantic information between KG and UIG, resulting in the above noise problem.