Knowledge-aware attentional neural network for explainable recommendation

抄録

We propose a knowledge-aware attentional neural network (KANN) for dealing with recommendation tasks by extracting knowledge entities from user reviews and capturing understandable interactions between users and items at the knowledge level. The proposed KANN can not only capture the inner attention among user (item) reviews but also compute the outer attention values between users and items before generating corresponding latent vector representations. These characteristics enable the explicit preferences of users for items to be learned and understood. Furthermore, our results and analyses highlight the relatively high effectiveness and reliability of KANN for explainable recommendation. Our code is publicly released at https://github.com/liuyuncoder/KANN.