I'm wondering how to setup the feature columns for DIN where the individual behaviour is VarLen and the historic sequence will be VarLen of VarLen.
In you current DIN example you have an individual behaviour is associated with a single category id:
cate_id = np.array([1, 2, 2]) # 0 is mask value
hist_cate_id = np.array([[1, 2, 2, 0], [2, 2, 1, 0], [1, 2, 0, 0]])
Suppose instead there are multiple categories for an indivual behaviour like in the moviesLens data:
cate_id = np.array([[1, 2, 3, 0], [3, 2, 1, 0], [1, 2, 0, 0]]) # 0 is mask value
hist_cate_id = np.array([[[1, 2, 2, 0], [2, 2, 1, 0], [1, 2, 0, 0]], [[1, 2, 2, 0], [2, 2, 1, 0], [1, 2, 0, 0]],[[1, 2, 2, 0], [2, 2, 1, 0], [1, 0, 0, 0]]])
How should I setup the feature columns to handle this?
I'm wondering how to setup the feature columns for DIN where the individual behaviour is VarLen and the historic sequence will be VarLen of VarLen.
In you current DIN example you have an individual behaviour is associated with a single category id:
cate_id = np.array([1, 2, 2]) # 0 is mask valuehist_cate_id = np.array([[1, 2, 2, 0], [2, 2, 1, 0], [1, 2, 0, 0]])Suppose instead there are multiple categories for an indivual behaviour like in the moviesLens data:
cate_id = np.array([[1, 2, 3, 0], [3, 2, 1, 0], [1, 2, 0, 0]]) # 0 is mask valuehist_cate_id = np.array([[[1, 2, 2, 0], [2, 2, 1, 0], [1, 2, 0, 0]], [[1, 2, 2, 0], [2, 2, 1, 0], [1, 2, 0, 0]],[[1, 2, 2, 0], [2, 2, 1, 0], [1, 0, 0, 0]]])How should I setup the feature columns to handle this?