Mobile Recommender System using Knowledge based Filtering

In order to provide quick selection and decision making in the field of mobile phones marketing, a knowledge-based mobile recommender system along with a personal interest questionnaire is proposed in this research. Initially, the mobile technical data was collected, cleaned, selected, and converted, then assigned labels to the data according to several extracted rules according to users' needs. Then a hybrid model was proposed using classification algorithms such as decision tree, naive Bayes and generalized linear model. This hybrid model along with deep learning algorithms and random forest were used to classify cell phones separately. Finally, these models were evaluated in terms of accuracy. Users can choose their preferred mobile phone using the suggested model and enter their preferences using the Personal Interests Questionnaire.