User modeling is a task which customizes and adapts the systems to meet users’ specific needs. The user modeling is widely used in many areas. For example, in e-commerce, it is used for modeling consumers’ preferences (behaviours) then predicting their future preferences to recommend suitable products to them. In e-learning (e.g., intelligent tutoring systems - ITS), the user modeling is used to model the learners (students) to track/predict their performance/knowledge.

In this work, an approach which integrates forecasting model into matrix factorization model to take into account sequential/temporal effects in user modeling since users’ need/knowledge may change overtime is introduced. The model as well as how to use stochastic gradient descent to learn this model, then resulting with an algorithm are thoroughly presented. The proposed model is validated using several data sets which are extracted from both e-commerce and e-learning areas. Experimental results on these data sets show that the proposed approach performs nicely. This could be a promising approach for user modeling.