A post-process for recommender systems: Multi-categorization recommendation adjusting (MRA)

With the continuous development of recommender systems technology, people´s reliance and demand on recommender systems is increasing, while traditional recommendation models have been unable to meet this increasingly diversified demand. This paper designs a general improved model to take advantage of the additional information, especially the essential information of objective product category, other than the traditional rating information contained in data, in order to improve the performance of recommender systems and meet the demand. This improved model is to optimize the results of recommendation based on traditional recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide controllable adjustment range, which thereby makes it possible to provide optional modes of recommendation aiming different kinds of users.