  The complementary item recommender system (CIRS) recommends the complementary
items for a given query item. Existing CIRS models consider the item
co-purchase signal as a proxy of the complementary relationship due to the lack
of human-curated labels from the huge transaction records. These methods
represent items in a complementary embedding space and model the complementary
relationship as a point estimation of the similarity between items vectors.
However, co-purchased items are not necessarily complementary to each other.
For example, customers may frequently purchase bananas and bottled water within
the same transaction, but these two items are not complementary. Hence, using
co-purchase signals directly as labels will aggravate the model performance. On
the other hand, the model evaluation will not be trustworthy if the labels for
evaluation are not reflecting the true complementary relatedness. To address
the above challenges from noisy labeling of the copurchase data, we model the
co-purchases of two items as a Gaussian distribution, where the mean denotes
the co-purchases from the complementary relatedness, and covariance denotes the
co-purchases from the noise. To do so, we represent each item as a Gaussian
embedding and parameterize the Gaussian distribution of co-purchases by the
means and covariances from item Gaussian embedding. To reduce the impact of the
noisy labels during evaluation, we propose an independence test-based method to
generate a trustworthy label set with certain confidence. Our extensive
experiments on both the publicly available dataset and the large-scale
real-world dataset justify the effectiveness of our proposed model in
complementary item recommendations compared with the state-of-the-art models.
