Multiple Criteria Recommendation Algorithm Based on Matrix Factorization and Random Forest

Nowadays, most of the recommendation algorithms recommend items similar to the ones the user preferred in the past with a single criterion, ignoring the multiple features of user preferences. By comparison, the multiple criteria based recommendation algorithm can achieve higher prediction rate by considering user preferences in multiple aspects of items. Hotel business is an important part of the tourism industry. In order to enhance the user experience and achieve a better predictive rating system, a multiple criteria recommendation algorithm based on matrix decomposition and random forest is proposed. The algorithm is implemented in two steps, through the matrix factorization models, each criterion gets an eigenvalue to every user and item. Eigenvalues of all criteria combine into an eigenvector. Then, random forest will be trained by the data set consisting of the eigenvector. The experimental results show that compared with the traditional collaborative filtering algorithms, the multiple criteria recommendation algorithm based on matrix factorization and random forest is better in terms of prediction and accuracy.