[Question] Item-to-Item recommendations with the candidate model

Hi !

On the retrieval tutorial page, item-to-item recommendations are mentionned at the end of the page. The paragraph suggests to use items for both towers and then train model with pair items.

I was wondering in the case where we don't have those kind of data still available, is it possible to do item-to-item recommendations with a query tower trained with users by just using the trained candidate tower in the BruteForce (see code below) ? I experienced this and results seem correct.

index_items = tfrs.layers.factorized_top_k.BruteForce(model.candidate_model, 200)
index_items.index_from_dataset(
    items.batch(100).map(lambda x: (x["id_house"], model.candidate_model({
                    "id_house": x["id_house"],
                    "type_logement": x["type_logement"],
                    "confort": x["confort"],
                    "capacity": x["capacity"] }))))

I assume I can get recommended items from different categories since different items could be of the same interest for same users and then be close in the embedding. To filter results, I simply put some filters behind the results according to the item alongside recommendations are displayed. This is maybe not the most effecient or cleanest way to proceed, but this seem to work as a simple solution.

So, my questions are what is your opinion about this approach ? Should the query and candidate towers always be used together as they are trained together ?

Thanks in advance for your time !
Jérémy

Start with the retrieval tutorial page linked in the issue and the BruteForce entry point shown in the example. Compare how the candidate and query towers are used, then document the supported approach and its limitations so the tutorial answers the item-to-item recommendation question.