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I want to solve this data mining problem:

  1. There is a sequence $S(t) = [A_1,...,A_{t-1}]$ .
  2. Each $A_i \subset U$ are set.
  3. Current time is $t \in \mathbb{N}$.
  4. $B$ is a subset of $A_t$. ($B \subset A_t$)
  5. $U$ is possible choices of a player.
  6. $I(S(t)) = \{1,...,t-1\}$ is indices of the sets in sequence.
  7. We can use $B$, $S(t)$ and $I(S(t)) \cup \{t\}$ to predict it .
  8. We already know $A_1,...,A_{t-1},B $ but we don't know $A_t-B$.
  9. Each element $x \in A_i$ only have ID of element. i.e. $x \in \mathbb{N}$.
  10. Objective is to predict $A_t - B$.

For example, a user can input tags as $A_i$ like $A_i =\{software, python \}$ and we call it "post". He already posted $A_1,...,A_{t-1}$ and inputted $B$. We want to predict tags on current post $t$.

Or another example is book-recommendation. A user bought books like $A_i = \{bookA, bookB\}$ and $i$ is a time. If he already selects some books on current time $t$, it means $|B|>0$, and the purpose is to recommend $A_t-B$.

I've tried to build a co-occurrence graph with frequency weight, but its accuracy was not so good.

Could you tell me how to solve it?

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