  Click-Through Rate (CTR) prediction plays an important role in many
industrial applications, and recently a lot of attention is paid to the deep
interest models which use attention mechanism to capture user interests from
historical behaviors. However, most current models are based on sequential
models which truncate the behavior sequences by a fixed length, thus have
difficulties in handling very long behavior sequences. Another big problem is
that sequences with the same length can be quite different in terms of time,
carrying completely different meanings. In this paper, we propose a
non-sequential approach to tackle the above problems. Specifically, we first
represent the behavior data in a sparse key-vector format, where the vector
contains rich behavior info such as time, count and category. Next, we enhance
the Deep Interest Network to take such rich information into account by a novel
attention network. The sparse representation makes it practical to handle large
scale long behavior sequences. Finally, we introduce a multidimensional
partition framework to mine behavior interactions. The framework can partition
data into custom designed time buckets to capture the interactions among
information aggregated in different time buckets. Similarly, it can also
partition the data into different categories and capture the interactions among
them. Experiments are conducted on two public datasets: one is an advertising
dataset and the other is a production recommender dataset. Our models
outperform other state-of-the-art models on both datasets.
