# A Hybrid Ontology-Based Recommendation System in e-Commerce
## 1 Introduction  
Over the last years, due to the growth of the Internet, the amount of data found in web pages and social networks has increased significantly. Hence, although users have more information available to them, it becomes more and more difficult to meet their demands when it comes to offering information related to their preferences [1]. The growth of the Internet has also potentiated the proliferation of e-services over numerous online platforms, whose main advantage is to provide products and services anywhere and anytime to users who have not yet purchased them [2]. With this amount of data and services available, it is not only difficult for users to quickly find items in which they are interested in, but also for e-commerce and similar systems to recommend items among the available data. In order to resolve these problems, recommendation systems have been offered as a solution. One of the most common successful methods in recommendation systems is collaborative filtering [3], which consists of recommending products and items that have similar preferences among users to the active user who liked or purchased them in the past. Another common recommendation system method is content-based [4], which consists of recommending products or items with similar characteristics to those already purchased by the active user. The main problem of this method is that the products recommended by the system will probably be very similar to those the active user has already purchased. Another common group of recommenders is knowledge-based recommenders, which consists of recommending items to the active user according to the domain knowledge about items that meet theuser's preferences [4]. One type of knowledge-based recommenders is ontology-based, which can be defined as an explicit specification of a conceptualization [5]. The ontology-based recommenders are knowledge-based recommenders that use an ontology to represent knowledge about the user and items [4]. The ontology represents knowledge about the user, the products, and the relationship between them. Current research demonstrates that the ontology-based method improves recommendation systems by resolving the most common limitations of traditional systems. However, building an ontology-based recommendation system is a complex and time-consuming task, which demands knowledge engineering skills [4; 6]. In this paper, we propose an innovative recommendation system approach, which combines the simplicity of the most prevalent algorithm in collaborative filtering, which is the K-Nearest Neighbor algorithm (KNN) [4] with the efficiency of ontology-based recommenders. Fundamentally, we create four main classes: Person, User, Neighbour, and Product. These classes are used to represent and model knowledge and details about each active user and the relationship between them. In order to recommend items to the active user, firstly we apply the KNN algorithm to find the nearest neighbors, and then we reapply it to find the nearest Products to recommend to the active user. The main contributions of this work are the following:  
* We propose a new e-commerce recommendation system, which combines collaborative filtering with ontology-based recommenders;
* The proposed recommendation system considers not only the users with similar preferences to the active user but also obtains knowledge about the user, their neighbours, products and the relationship between them;
* Our proposal increases the number of recommended products from categories which the active user has not yet purchased;
* The proposed system is scalable, which means that it maintains good level performance when the workload increases. The rest of this paper is organized as follows. Section 2 presents some related work, focused on ontology-based and collaborative filtering recommenders. Section 3 describes the hybrid ontology-based approach, namely, the ontology classes and their properties, and the application of the KNN to find the K nearest Neighbours and Products.