User interests profiling using fuzzy regression tree. (22nd August 2022) - Silem, Abd El Heq; Taktak, Hajer; Moussa, Faouzi
User modelling is an essential process for recommender systems. In user interests modelling, two kinds of approaches can be found. The first uses the text extracted from the user's browsing history to predict his interest degree, while the second, besides the extracted text, adds the user actions as input. Both groups predict an incorrect interest degree due to the use of text-only or an incorrect assessment of the user's actions. This paper proposes an architecture that employs a fuzzy regression tree algorithm to model the user's interests. The architecture detects the user's interests by evaluating his behaviour based on these factors: scrolling speed, time spent, location, number of visits, and clicked links. It also uses fuzzy logic to ensure the correct interpretation of each factor value according to the user's habits. Finally, the results show that the proposed architecture has the minimum RMSE (≈0.06) compared to the existing solutions.