The first group of companies that have a business on online social networks try to design an efficient plan for making more money on this platform. Advertising can be a solution for introducing and promoting the services or products for the clients and it can be led to more sells. There are a second group of companies intended to use advertisements on social networks, many of these annoy the users since they are not fascinating or matched for the clients. The primary target of the current study is to design and present a model of advertising recommender systems on social networks using innovative techniques. Although there are numerous applications and research works about recommender frameworks, in the proposed model, it is valuable to plan a recommender system which focus more precisely on the user's interests. The framework uses a semantic logic to increase the accuracy of the recommendations along with using a combination of four recommender methods, the particular estimations for each method and the integration of recommendations generated by each method using a rank-based approach which totally can differentiate the proposed recommender framework from the previous similar methods. The accuracy of suggested framework is 0,7498 that was revealed by implementing a web application. The comparison of some similar models with the current work based on various features and aspects shows a significant excellence of this study.