Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly.
Reasoning-based methods with complex reasoning mechanisms, such as reinforcement learning-based sequential decision making, have been regarded as the default pathway for this task.
Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users.
Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology.
The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias.
We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.
Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems.