# A Hybrid Ontology-Based Recommendation System in e-Commerce
## 2 Related Work
In order to solve these problems, it is suggested that an improvement in Slop One algorithm be made based on trustworthy data and user similarity. Although the proposal solves the common problems of the basic Slop One algorithm, the use of trustworthy data and user similarity only takes into concern to the ratings given to the products, which means that the recommendation process does not include the preferences of the active user, neither the features of the products. The development of a utility-based recommender system to deal with clothing recommendations is presented in [15]. This work uses the ratings given by the users to the products implicitly, which avoids user effort. Although the process of recommendation seems to be original, it will recommend products very similar to the ones that the active user already purchased, which our work looks to avoid. Moreover, in e-commerce platforms that deal with a huge number of products and where these differ from the number of attributes and features, the proposed approach does not work. [PERSON] et al. [16] propose a collaborative filtering approach to recommend products based on active user' ratings. The proposed approach is the use of basic collaborative filtering without any improvement. After selecting the top products to recommend to the active user, the proposed approach finds the nearby store, which has the recommended product. The proposed approach only works in e-commerce platforms that have the location of the products and the location of the active user. This work differs from ours on the basis that it only focuses on the ratings given to the products, hence, it does not include the preferences of the active user, neither the features of the products. A clothing recommendation system based on the improvement of user-based collaborative filtering algorithm is defined in [17]. In order to improve the basic user-based collaborative filtering algorithm, the authors define a better calculus of similarity among users and also a user-item linked list to reduce sparseness of the matrix. This work gives more importance to the products that are unpopular, but it does not specify the percentage of products in the chosen dataset, which raises doubts about the overall quality of these products. Furthermore, the use of a linked list can perform well on small datasets, however, when dealing with large amounts of data, this approach will increase the time of the recommendation process since the entire linked list will have to be completely scrolled. This work also only focuses on ratings given to the products, which means that the recommendation process does not include the preferences of the active user, neither the features of the products. [PERSON] and [PERSON] [18], describe a hybrid recommendation system which combines collaborative filtering and content-based filtering together with contextual information like the weather or the season of the year. The experimental evaluation shows that this hybrid approach can improve the quality of recommendation systems over the traditional approaches. The main difference between this work and ours is that the first one requires an unnecessarily large amount of complexity to build the proposed approach since it uses three recommendation techniques along with a neural network. [PERSON] et al. [19], present a hybrid recommendation system which combines collaborative filtering with ontology and dimensionality reduction techniques in order to improve the sparsity and time complexity of the collaborative filtering approach. The experimental evaluation shows the benefits of building a hybrid recommendation system when compared to the traditional systems. Apart from the similarities to our work, this approach focuses more on the ratings given to movies. In addition, it does not give attention to the diversity of the movie's recommendation, which is one of our main contributions.