Fusing User Reviews Into Heterogeneous Information Network Recommendation Model

With the advent of the information epoch and the development of Big Data, users are constantly overwhelmed in massive information online. As an effective method to deal with this dilemma of information overload, recommendation system has become a popular area of research for the past few years. However, most of the current recommendation models fail to make the most of the ample resources hidden behind auxiliary data and user reviews. For this reason, we put forward the FHRec model. We try to combine heterogeneous information network with deep learning technology to improve recommendation performance. The working mechanism is to represent the rich auxiliary data by using heterogeneous information network, learning the features of entities through network embedding; in the meantime, by adopting deep learning technology to mine the features of entities from review texts, using the attention mechanism to fuse feature vectors of entities, inputting them into the neural network for recommendation; the last step is that we use Yelp dataset and Douban movie dataset to verify the FHRec model. The experimental results show that the FHRec behaves better than the traditional comparison algorithm on the Yelp dataset and Douban movie dataset.