A Recommendation System with Fusion Relation Extraction

Aiming at the problem of insufficient knowledge utilization in the existing content-based recommendation methods, a recommendation system based on fusion relation extraction was proposed in this paper. Using word2vec model to encode object knowledge, using supplementary template features to excavate the object knowledge in a deeper level, an enhanced knowledge graph was constructed. Moreover the enhanced entity features were obtained, being combined with text features and basic entity features to construct object features. Experimental results show that the recommendation effect based on fusion relation extraction is better than that of the similar models, and the improvement of each part is effective.