# A Hybrid Ontology-Based Recommendation System in e-Commerce
## 3 The Hybrid Ontology-based Approach
### Rules Approach
In order to understand if the products recommended by the proposed approach are products that have received a high rating by all the users who have bought them, we extract the average overall rating given to each product that is recommended in the two versions of the recommendation system. Consequently, we conclude, by observing of the Average Overall column in Figures 6 and 7, that version A of the recommendation system can recommend new products to the active user that received a better overall rating when compared to the products recommended by version B. In the experimental evaluation, we tested the combination of three values for the k-nearest Neighbours and the k-nearest Products. The results show that the combination, which achieved best results is when k is 5 in both KNN applications. In other words, when we choose to find the 5 nearest Neighbours of the active user and recommend the 5 nearest Products is when the products recommended match user's preferences and belong to new categories to the active user. From the experimental results, it is also possible to conclude that in version A of the recommendation system, the number of products which the KNN algorithm applies to find the nearest ones is lower when compared to version B. Consequently, the version A of the recommendation system can find Neighbours that are similar to the active user, which is translated into products that better satisfy the active user's preferences and demands. Contrariwise, in spite of version B of the recommendation system provides a higher number of Products to which the KNN algorithm will be applied to, those products don't easily match the user preferences and belong to categories to which the active user has already bought products from. Figure 8 presents the importance of the TopFiveWords attribute of each Product to provide better recommendations. For each product recommended by the hybrid ontology-based, the figure shows the relation between the number of the 5 most common words that are in the 200 most common words used in the reviews with a rating of 4 or 5 in the respective category, and the average overall rating given by all the users that have bought the product. From Figure 8, we can affirm that most of the times the TopFiveWords attribute it is similar to the average overall rating given to that product. Therefore, when the average overall rating of the Product increases the TopFiveWords also increases and the same for when the average overall rating goes down. ### Discussion of the Results

From the the experimental evaluation, we can conclude that the hybrid ontology-based recommender, proposed in this paper, improves the recommendation of Products in e-commerce. Mainly, it increases the number of Products recommended that belong to unknown categories to the

Figure 8: Relation between TopFiveWords and average overall of each Product. active User. The Products that are recommended to the active User also match his preferences more easily when compared to the collaborative filtering version of the recommendation system. Even when the active User has only bought one Product, which causes some missing features of the active User, such as the MinPrice and Max Price being equal. However, our hybrid ontology-based recommender consumes more time to apply KNN to find the k-nearest Products. As explained previously, this is strongly related to the time consuming for extracting the 5 most common words used in all the reviews of that product, which consists of extracting the words, removing stop words, applying Lemmatization and then finding the 5 most common. In inverse, the time to apply KNN to find the k-nearest Products in collaborative filtering is very low, because it only focuses on choosing the Products with the highest ratings given by the respective Neighbours. ## 5 Conclusions and Future Work

We proposed a new approach to recommend Products in e-commerce systems, which combines the simplicity of finding users that have similar preferences with the active user, with ontology-based models.