I am trying to build a recommendation model by using a tensor. In order to recommend an article to a user, I have built a model to predict users' preferable articles.
I am labeling articles based on topics, the result of my learning dataset is a 3-dimensions dataset (USers (X) - Topics (Y) - Time (Z) ), because user's interest might evolve over time, at the same time it doesn't change much.
In order to build predictive models, I usually deal with 2-Dimensions data X,Y and predict based on training/Testing. Now, I am trying to lean how to use the different techniques that might help solving similar problems (like Matrix Factorizations and tensors, Tensor Decompositio, convex tensor completion methods for tensors )
I am little bit lost in literature, what are some good resources (courses/tutorials) to learn more on how to use tensors and related techniques to model similar above-mentioned problems ?