I am studying and experimenting with TFRS and collaborative filtering.
I see in the retriveal and ranking models tutorials, which are using the movielens dataset that interactions are a movie watch and a movie ranking.
That is a single <user, item> or <user, item, user_ranking> interaction pair.
How about if the data contains items that you can interact more than once? And you can rate every interaction?
<user1, item1, user_ranking1>,
<user1, item1, user_ranking2>
<user1, item1, user_ranking3>
<user1, item2, user_ranking4>
<user1, item2, user_ranking5>
<user2, item3, user_ranking6>
and maybe the user can have different feature for each interaction i.e. he gave ranking1 at timestamp1, ranking2 at timestamp2 etc?
<user1, item1, user_ranking1, timestamp1>,
<user1, item1, user_ranking2, timestamp2>
<user1, item1, user_ranking3, timestamp3>
<user1, item2, user_ranking4, timestamp4>
<user1, item2, user_ranking5, timestamp5>
<user2, item3, user_ranking6, timestamp56>
Can we model such data and feed in the TFRS ranking/retrieval model?