# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 2 Related Work  
Web service recommendation for mashup construction has been widely researched in recent years. Similar to the taxonomy of general recommender system approaches [5], the service recommendation approach can be divided into three categories: collaborative filtering (CF) based approach, content-based approach, and hybrid approach [19]. CF-based approaches recommend services from the historical composition data of the constructed mashups and their services. It can be further divided into neighborhood based approaches and model-based approaches such as matrix factorization. For example, [PERSON] et al. propose a coupled matrix factorization model to predict the multi-dimensional relations among users, mashups, and services [6]. [PERSON] et al. propose using a generalized manifold ranking algorithm on the graph with relations among mashups and services [20]. [PERSON] et al. measure the similarity between mashups based on a heterogeneous information network (HIN) with various meta-paths, and make recommendations similar to user-based CF [7]. Based on this model, [PERSON] et al. propose to measure functional similarities between mashups based on the word vectors of content learned with the word embedding technique [8]; [PERSON] et al. propose a mashup group preference-based service recommendation on HIN, where mashup group preference is utilized to capture the rich interactions among mashups [9]. They also propose to use factorization machines to model the features of mashups and services learned from different kinds of meta-paths on HIN [10]. Content-based approaches recommend services by matching the content similarities between candidate services and the mashup requirements. It can be further divided into three categories: keyword-based approach, latent semantic-based approach, and deep learning-based approach. Keyword-based approaches extract keywords or salient entities from the content and use them to match user requests. For instance, [PERSON] et al. propose to extract keywords from a keyword-candidate list and domain thesaurus to indicate users' preferences [11]. Latent semantic-based approaches usually extract content features using topic models. For instance, [PERSON] et al. propose Correlated Topic Model (CTM) to extract topics from semantic service descriptions and model their correlations [12]; [PERSON] et al. propose to extract textual features with latent dirichlet allocation (LDA) [21]. Deep learning-based approaches provide a more powerful representation capability for modeling content; thus, they have been widely adopted in recent work. For instance, [PERSON] et al. propose to use stacked denoising autoencoders (SDAE) to perform feature extraction from the content and impose the composition records as a regularization [13]; [PERSON] and [PERSON] propose a Long Short-Term Memory-based (LSTM) model to recommend services with a functional attention mechanism and a contextual attention mechanism [14]. Hybrid approaches recommend services by incorporating various factors including mashup-service composition history, functionalities of mashups and services, and other contextual information. [PERSON] et al. unify both collaborative filtering and content-based recommendations with both rating and content data of services using a probabilistic generative model [16]. [PERSON] et al. incorporate three factors, namely service functionality, usage history, and popularity for recommendation using topic models, matrix factorization, and Bayes' theorem [17]. [PERSON] et al. propose a model that integrates collaborative filtering and textual content into a deep neural network [18]. [PERSON] et al. propose a multiplex interaction-oriented service recommendation approach where they extract hidden structures and features from various types of interactions between mashups and services and incorporate them into a deep neural network [19]. [PERSON] et al.