movieRec | ShinyApps application of a movie recommendation system | Recommender System library

All recommendForAllUsers() does is

• Take the user blocks and item blocks (along with their vector embeddings) learnt at the time of training.
• Compute the cross join of user and item blocks.
• Calculate dot product of the embeddings.
• Select top n products based on the value of dot product, for each user.

In the above process, the cross join broadens the user-item space. Since the model was trained with those items (watched movies), the dot product for them will always be significant. Hence, the items appearing for a user at the training time appears at the time of recommendation for the same user.