# Multi-Feature Extension via Semi-Autoencoder for Personalized Recommendation
## 1 Introduction  
In the past decade, due to the explosive growth of the Internet and the resulting surge in data, people have easier access to a vast array of online products and multimedia content. While this growth allows users to have multiple choices, it also makes it difficult for users to choose their favorite among multiple candidates [1]. Recommendation System (RS) can effectively help users find valuable information and make decisions quickly to reduce information overload, which is recently utilized in e-commerce [2], e-learning [3], and intelligence education [4]. Among all the RS methods, Collaborative Filtering (CF) aims to achieve the feature representations of users and items and predicts new rating information with the feedback information. Traditional model-based CF methods commonly use machine learning models to extract hidden features between users and items and conduct appropriate recommendation models. Matrix Factorization (MF) models are the most used among these models [5]. However, the latent features learned by MF are generally inefficient, especially when the rating matrix is highly sparse. Along this line, there has already been some effort in devoting mining and exploiting hidden features of both implicit and explicit feature information to enhance recommendation accuracy. Motivated by the application of deep neural network in various fields, including speech recognition and computer vision [6], deep neural network based recommendation methods have a profound impact on practical applications due to their stronger latent feature representation learning capability and flexible model structure. However, the mostmodels still rely on considerable labeled data and the training time is prolonged [7]. AutoEncoder (AE), a type of artificial neural network, has recently observed a lot of work performed on top of them. AEs have also been actively used in CF models due to their superiority in non-labeled data requirements and quick convergence. While autoencoder-based methods have had some success in predicting the correct items for users--such as in Yelp, Netflix, or Amazon--significant challenges remain in the face of data sparsity, which directly affects RS performance. However, some methods have utilized auxiliary information for data sparsity [8; 9]. However, two major issues still hinder the further development of these methods. First, the auxiliary information available to users and items for feature extension is usually limited. Second, most existing methods ignore the hidden relations between extended features, which significantly affects the recommendation performance. To address the above issues, we propose a multi-feature extension method via a semi-autoencoder to improve personalized recommendation performance, called MFSAE. Considering that Knowledge Graph (KG) can produce reliable semantic retrieval data, we extract the auxiliary information of items through an open KG, which can be used as an additional attribute for the recommendation. To be more precise, we first extract the feature expansion information of items from DBpedia (an open KG based on Wikipedia). Secondly, we leverage the LSI model based on the Latent Semantic Analysis (LSA) model to learn hidden relations on top of item features and embed them into low-dimensional feature vectors. Finally, the resulting feature vectors, combined with the original rating matrix and side information, are fed into a semi-autoencoder for recommendation prediction. MFSAE enriches the content of auxiliary information and mines the similarity between expansion features to improve recommendation performance. Extensive experiments on two MovieLens datasets validate the effectiveness of MFSAE. Our contributions are outlined below in brief:  
* We propose a novel semi-autoencoder based recommendation method, which introduces an open KG to extend the auxiliary feature information of items, and the captured information can be used flexibly and conveniently.