# Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
## 1 Introduction  
Most of the existing collaborative filtering (CF)-based recommendation systems work with various unstructured data such as ratings, reviews, or images to profile the users for personalized recommendation. Though effective, it is difficult for existing approaches to model the explicit relationship between different information that we know about the users and items. In this paper, we would like to ask a key question, i.e., _\"can we extend the power of CF upon large-scale structured user behaviors?\"_. The main challenge to answer this question is how to effectively integrate different typesof user behaviors and item properties, while preserving the internal relationship between them to enhance the final performance of personalized recommendation.  
Fortunately, the emerging success on knowledge-base embeddings (KBE) may shed some light on this problem, where heterogeneous information can be projected into a unified low-dimensional embedding space. By encoding the rich information from multi-type user behaviors and item properties into the final user/item embeddings, we can enhance the recommendation performance while preserving the internal structure of the knowledge.  
Equipping recommender systems with structured knowledge also helps the system to generate informed explanations for the recommended items. Researchers have shown that providing personalized explanations for the recommendations helps to improve the persuasiveness, efficiency, effectiveness, and transparency of recommender systems [1; 2; 3; 4; 5; 6; 7]. By preserving the knowledge structure about users, items, and heterogenous entities, we can conduct fuzzy reasoning over the knowledge-base embeddings (KBE) to generate tailored explanations for each user.  
Inspired by the above motivations, in this paper, we design a novel explainable CF framework over knowledge graphs. The main building block is an integration of traditional CF with the learning of knowledge-base embeddings. More specifically, we first define the concept of user-item knowledge graph, which encodes our knowledge about the user behaviors and item properties as a relational graph structure. The user-item knowledge graph focuses on how to depict different types of user behaviors and item properties over heterogeneous entities in a unified framework. Then, we extend the design philosophy of CF to learn over the knowledge graph for personalized recommendation. For each recommended item, we further conduct fuzzy reasoning over the paths in the knowledge graph based on soft matching to construct personalized explanations.  
**Contributions.** The main contributions of this paper can be summarized as follows:  
* We propose to integrate heterogeneous multi-type user behaviors and knowledge of the items into a unified graph structure for recommendation.
* Based on the user-item knowledge graph, we extend traditional CF to learn over the heterogeneous knowledge for recommendation, which helps to capture the user preferences more comprehensively.
* We further propose a soft matching algorithm to construct explanations regarding the recommended items by searching over the paths in the graph embedding space.  
In the following part of the paper, we first present related work in Section 2, and then provide the problem formalization in Section 3. Section 4 goes over the model for CF over knowledge graphs, and in Section 5 the soft matching method for generating explanations is illustrated. Experimental setup and discussion of the results are provided in Section 6 and Section 7, respectively. We conclude the work and point out some of the future research directions in Section 8.