  Collaborative Metric Learning (CML) has recently emerged as a popular method
in recommendation systems (RS), closing the gap between metric learning and
Collaborative Filtering. Following the convention of RS, existing methods
exploit unique user representation in their model design. This paper focuses on
a challenging scenario where a user has multiple categories of interests. Under
this setting, we argue that the unique user representation might induce
preference bias, especially when the item category distribution is imbalanced.
To address this issue, we propose a novel method called
\textit{Diversity-Promoting Collaborative Metric Learning} (DPCML), with the
hope of considering the commonly ignored minority interest of the user. The key
idea behind DPCML is to include a multiple set of representations for each user
in the system. Based on this embedding paradigm, user preference toward an item
is aggregated from different embeddings by taking the minimum item-user
distance among the user embedding set. Furthermore, we observe that the
diversity of the embeddings for the same user also plays an essential role in
the model. To this end, we propose a \textit{diversity control regularization}
term to accommodate the multi-vector representation strategy better.
Theoretically, we show that DPCML could generalize well to unseen test data by
tackling the challenge of the annoying operation that comes from the minimum
value. Experiments over a range of benchmark datasets speak to the efficacy of
DPCML.
