Enhancing recommendations with contrastive learning from collaborative knowledge graph https://doi.org/10.1016/j.neucom.2022.12.032 Abstract There have been excellent results using knowledge graphs in recommender systems. Knowledge graphs can be used as auxiliary information to alleviate data sparsity and strengthen the modeling of item sets and the representation of user preferences. However, users as the Core subject in the recommendation process, should be taken seriously. We believe that the user's choice of items will be affected by internal and external factors. Internal factors refer to the users’ fuzzy interest sets, which initially affect the users' choices. External factors refer to the influence of similar users and similar items in the users' selection of items. [Os itens recomendados ainda passam por um processo de decisão do usuário] Introduction There is a lot of knowledge on today's Internet. Generally speaking, this knowledge is not isolated but interre...