Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph Representation Learning

To address the challenges of cold start and data sparsity that traditional recommendation systems face when using matrix decomposition collaborative filtering techniques, the author suggests a novel recommendation algorithm based on knowledge graph representation learning. This algorithm leverages knowledge graphs to encode learning methods, transforming rich semantic data into a compact, low-dimensional semantic space. This process enables the computation of semantic similarity between different items and seamlessly incorporates semantic information into collaborative filtering recommendations. This algorithm takes into account the inherent knowledge associated with each item, thereby enhancing the effectiveness of collaborative filtering recommendations at a semantic level. By amalgamating semantic information with user-item interaction data, the system gains a more precise understanding of users' interests and preferences, leading to more personalized recommendations. Experimental findings substantiate that the author's algorithm significantly improves the accuracy and recall of collaborative filtering recommendations. This advancement results in more profound and intelligent recommendations, addressing the shortcomings of traditional approaches.