An Effective Model for Improving the Quality of Recommender Systems in Mobile E-Tourism

In major e-commerce recommendation systems, the number of users and items is very large and available data are insufficient for identifying similar users. As a result, recommender systems could not use users' opinion to make suggestions to other users and the quality of the recommendations might reduce. The main objective of our research is to provide high quality recommendations even when sufficient data are unavailable. In this article we have presented a model for this condition that combines recommendation methods (e.g., Collaborative Filtering (CF) and Content Based Filtering (CBF)) with other methods such as clustering and association rules. The model consists of four phases, at the first phase, tourists are clustered based on their location and the target tourist's cluster is sent to the next phase. In the second phase, a two level graph is made based on the similarity between the tourist interests and the similarity of the tours. According to this graph, transitive relations are discovered among the tourists and k number of items that have the highest weight of relationships and are suggested to the target tourists. According to the experiments, the standard F-measure indicates that the quality of the recommendations of this model is higher than the traditional approaches which cannot discover transitive relationships.