  Bayesian Personalized Ranking (BPR) is a representative pairwise learning
method for optimizing recommendation models. It is widely known that the
performance of BPR depends largely on the quality of negative sampler. In this
paper, we make two contributions with respect to BPR. First, we find that
sampling negative items from the whole space is unnecessary and may even
degrade the performance. Second, focusing on the purchase feedback of
E-commerce, we propose an effective sampler for BPR by leveraging the
additional view data. In our proposed sampler, users' viewed interactions are
considered as an intermediate feedback between those purchased and unobserved
interactions. The pairwise rankings of user preference among these three types
of interactions are jointly learned, and a user-oriented weighting strategy is
considered during learning process, which is more effective and flexible.
Compared to the vanilla BPR that applies a uniform sampler on all candidates,
our view-enhanced sampler enhances BPR with a relative improvement over 37.03%
and 16.40% on two real-world datasets. Our study demonstrates the importance of
considering users' additional feedback when modeling their preference on
different items, which avoids sampling negative items indiscriminately and
inefficiently.
