  When a user finds an interesting recommendation in a recommender system, the
user may want to recall related items recommended in the past to reconsider or
to enjoy them again. If the system can pick up such "recalled" items at each
user's request, it must deepen the user experience.
  We propose a model and the algorithm for such personalized "recalling" in
conventional recommender systems, which is an application of neural networks
for associative memory. In our model, the "recalled" items can reflect each
user's personality beyond naive similarities between items.
