CBOW: We are trying to predict the next word based on the context (defined as a certain window of words around the target word)
RNN can also be used for predicting the next word in a sequence, where each time the input is the present input and the recent past (i.e. output of the previous step)
I am not able to understand how the RNN's approach is somehow better, because I could define a very large window for CBOW, and then it would also predict words based on previous information. Why do we say RNN will be better at predicting because it has memory? If we supply everything to CBOW at the same time, which is given to RNN in steps, why is one better than the other? Is it only because we save on computation in the latter case?