  Representation learning-based recommendation models play a dominant role
among recommendation techniques. However, most of the existing methods assume
both historical interactions and embedding dimensions are independent of each
other, and thus regrettably ignore the high-order interaction information among
historical interactions and embedding dimensions. In this paper, we propose a
novel representation learning-based model called COMET (COnvolutional diMEnsion
inTeraction), which simultaneously models the high-order interaction patterns
among historical interactions and embedding dimensions. To be specific, COMET
stacks the embeddings of historical interactions horizontally at first, which
results in two "embedding maps". In this way, internal interactions and
dimensional interactions can be exploited by convolutional neural networks
(CNN) with kernels of different sizes simultaneously. A fully-connected
multi-layer perceptron (MLP) is then applied to obtain two interaction vectors.
Lastly, the representations of users and items are enriched by the learnt
interaction vectors, which can further be used to produce the final prediction.
Extensive experiments and ablation studies on various public implicit feedback
datasets clearly demonstrate the effectiveness and rationality of our proposed
method.
