# A Hybrid Ontology-Based Recommendation System in e-Commerce
## 3 The Hybrid Ontology-based Approach
### Rules Approach
This approach generates more knowledge about the active User, the respective Neighbours and the Products recommended to them (the user) and the relation between these three. The experimental evaluation shows that our hybrid ontology-based system can recommend, in comparison with the collaborative filtering version, products that match user preferences more easily and belong to new categories for the active user. The proposed approach also chooses the k-Nearest Neighbours of the active user, which is translated in a small number of products to which KNN will be applied to. However, it satisfies the active user's preferences, when compared to the collaborative filtering version, which provides a large number of products to which KNN will be applied, but most of the time, belong to categories, which that active user already knows. The experimental evaluation also shows that the hybrid ontology-based recommender, in comparison with the collaborative filtering version, can recommend products, which have received a higher average overall rating, to the active user. Despite the good results achieved by the hybrid ontology-based recommendation system, the time to apply the KNN to find the k-nearest Products is too high. For this reason, as in future work, in order to reduce the time of applying the KNN algorithm to find the k-nearest Products, we intend to find the 5 most common words used in the all the reviews of each product and save this attribute in the database, which will dramatically reduce the time of applying the KNN algorithm to the Products. Although generally, the system can discover the active users's gender based on the reviewer name, it would be interesting to find a way to discover his/her gender when the reviewer's name is a nickname or is based on numbers. This procedure might increase the quality of the recommended products. As future work, we also intend to apply the proposed approach to the entire dataset, which as previously mentioned, consists of 142.8 million reviews. In order to apply the KNN algorithm to find the k-nearest products, we also plan to take into consideration of the vector of features' creation and the average overall rating of each product. We also intend to improve our models by adding some extra features. In this paper, we have worked, with English texts, then we extend this to other language texts. We will also try to detect other sentiment labels of the human being and at the same time, working with a big amount of texts. Finally, it would be of interest to work with other datasets and perform evaluation based on more measures such as accuracy, precision, recall, MAE (Mean Absolute Error), and also NDCG (Normalized Discounted Cumulative Gain). Conceptualization, R.R.S. and J.B.; Methodology, M.G. and R.R.S. ; Software, M.G. ; Validation, M.G., R.R.S. and J.B.; Formal analysis, M.G., R.R.S. and J.B.; Investigation, M.G. ; Resources, M.G. ; Data curation, M.G. ; Writing--original draft preparation, M.G. ; Writing--review and editing, M.G., R.R.S. and J.B.; Supervision, R.R.S. and J.B.; Project administration, R.R.S. and J.B.; Funding acquisition, J.B. This research received no external funding. **Acknowledgments:** We acknowledge Centre of Informatics and Systems of University of Coimbra (CISUC) for the facilities offered during this project. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON] The state-of-the-art in expert recommendation systems. _Eng. Appl. Artif. Intell._**2019**, _82_, 126-147. [CrossRef]
* (2) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Ontology-Based Recommender System in E-Commerce. In Proceedings of the 2 nd International Workshop on Recommender Systems meet Big Data & Semantic Technologies, Hong Kong, China, 12-16 October 2013; pp. 4-6. * (3) [PERSON]; [PERSON]; [PERSON]; [PERSON] An improved collaborative filtering method based on similarity. _PLoS ONE_**2018**, _13_, e0204003.