Recommendation Systems Based on Online User´s Action

In this paper, we propose a new recommender algorithm based on multi-dimensional users behavior and new measurements. It´s used in the framework of our recommender system that use knowledge discovery techniques to the problem of making product recommendations during a live user interaction. Most of Collaborative filtering algorithms based on user´s rating or similar item that other users bought, we propose to combine all user´s action to predict recommendation. These systems are achieving widespread success in E-tourism nowadays. We evaluate our algorithm on tourism dataset. Evaluations have shown good results. We compared our algorithm to Slope One and Weight Slope One. We obtained an improvement of 5% in precision and recall. And an improvement of 12% in RMSE and nDCG.