A Feature Extraction Based Recommender Algorithm Fusing Semantic Analysis

Recommender system is an effective way to deal with the problem of personalized recommendations. Most existing recommendation methods have insufficient power to analysize inherent characteristics of users and items. To alleviate the problem, a feature extraction based recommender algorithm that fuses semantic analysis is proposed in this paper, which involves knowledge graph as heterogeneous information to enhance semantic analysis of collaborative filtering. First of all, the named entity recognition (NER) and entity linking (EL) are used to extract entities and relations about a certain item from its unstructured text information, and we construct a subgraph based on these identified entities and relations. Then we embed the subgraph to a low latent vector space by the technology of knowledge graph embedding for an easier expression. After that, the embedding results are used to represent users and items, and we design a knowledge aware collaborative learning framework to learn the fine-grained features of users and items. Finally, the embedding results are used to make Top-N recommendations for a target user. Experimental results based on two datasets show that our new framework is able to improve the recommender accuracy compared with some state-of-the-art models. It means that our new method is able to recommender items which are better matches in users’ preferences.