# A Hybrid Ontology-Based Recommendation System in e-Commerce
## 2 Related Work  
In this section, we present a summary of the research papers that focus on ontology-based and collaborative filtering recommenders, in different areas, such as e-commerce, e-learning, and social events. [PERSON] et al. [7], propose a hybrid recommendation system, which combines an ontology-based recommender with machine learning techniques. The main goal of the proposed approach is to recommend universities to the students using an ontology to represent domain knowledge about the universities and students, and machine learning techniques to perform the recommendation. The proposed approach not only focuses on the students' grades, but also on their skills and interests, which is an innovative idea. However, it is necessary to perform an experimental evaluation to confirm the effectiveness of the hybrid recommendation system. [PERSON] et al. [8], suggest an ontology-based recommender that aims to recommend laptops to users who are not aware of low-level specifications. The proposed approach uses the ontology to map functional requirements with low-level specifications. Basically, the system asks functional requirements to the user according to their preferences and then the ontology maps those requirements with low-level specifications to find laptops that match the active user's preferences. Although it is an innovative idea, this approach requires an effort of the active user, who needs to introduce the functional requirements of the intended laptop. Therefore, when the active user does not know enough functional requirements, the recommendation system does not work. The experimental evaluation shows that the proposed approach achieves an accuracy of 84.6% when compared to general recommender systems that achieve a low accuracy of 61.5%. However, the experimental evaluation was performed with only 39 users, which is not a large enough number to conclude the improvement of accuracy. [PERSON] et al. [9], propose an approach that uses an ontology-based recommender to provide schools that matches the active user's preferences. This work uses the active user comments on their Facebook feed about a certain school to extract features that allow the creation of an ontology-based recommender, based on the polarity of the comments. Moreover, the recommendation process can recommend schools based on certain branches chosen by the active user. Despite the differences between this work and our proposal, this paper allows to perceive that ontology-based recommender can improve the quality of the recommendation process. [PERSON] et al. [2], present an approach that combines the use of two ontologies, in order to retrieve e-services' descriptions, such as the products' commercial features, and creates a user profile that is composed of transactional data, user personality, habits, and preferences. The aim of the proposed approach is to recommend products to users that match the user profile information together with the products and services description. Although the proposed approach seems to be original, there is no experimental evaluation to confirm the proposed idea. An approach to classify events is presented in [10], which uses an ontology to define the most important properties in describing an event, based on the LODE ontology [11]. Instead of classifying an event into categories, this work aims to classify an event according to tags. For experimental evaluation, a synthetic dataset based is generated on real data from a popular social network. The results when compared to the LODE ontology, show an improvement of correctly classified events, a decrease of memory consumption and better time performance. Although the proposed idea achieved good results and proved the advantages of the usage of the ontology, experimental evaluation with real data is not presented. [PERSON] et al. [3], propose an approach to improve the collaborative filtering recommender. The main goal is to improve the accuracy and quality of the recommendation system, mainly when the sparsity of data is a problem.