A recommendation algorithm based on fine-grained feature analysis

Abstract

Most existing recommender methods have insufficient power to capture or recover fine-grained features of products and user preferences. Knowledge graphs contain considerable information about products and mutual relationships in the world. To analyze the fine-grained features during the process of recommendation, we propose a knowledge-aware collaborative learning framework (KACL), which is the first work to combine users’ historical reviews with knowledge graphs. First, we use a named-entity recognition (NER) system to recognize the named entities that correspond to item features in the unstructured reviews. After that, we use an entity-linking (EL) system to map entities which identified in the first step to the corresponding entity in Wikipedia. Next, we constructed a sub-graph that depends on the extracted entity and the related ones and embedded the sub-graph into a low latent vector space as pretrain item embedding through the knowledge graph embedding model TransR. In the next step, we will fine-tune item embedding through deep learning built by 4 levels of relu and combine collaborative information with pretrain item embedding. And in accordance with the user review behavior, the item embedding will be averagely poled to express the user embedding, we will put the item embedding and the user embedding into the classification model. Finally, we use the KACL to integrate collaborative filtering with entity representation and make recommendations. Experiments are performed on real-world datasets to evaluate the effectiveness of our new method. The results show that the analysis of fine-grained features based on a knowledge graph helps our KACL improve recommender accuracy compared with the selected state-of-the-art models.