  Sequential recommendation is a popular task in academic research and close to
real-world application scenarios, where the goal is to predict the next
action(s) of the user based on his/her previous sequence of actions. In the
training process of recommender systems, the loss function plays an essential
role in guiding the optimization of recommendation models to generate accurate
suggestions for users. However, most existing sequential recommendation
techniques focus on designing algorithms or neural network architectures, and
few efforts have been made to tailor loss functions that fit naturally into the
practical application scenario of sequential recommender systems.
  Ranking-based losses, such as cross-entropy and Bayesian Personalized Ranking
(BPR) are widely used in the sequential recommendation area. We argue that such
objective functions suffer from two inherent drawbacks: i) the dependencies
among elements of a sequence are overlooked in these loss formulations; ii)
instead of balancing accuracy (quality) and diversity, only generating accurate
results has been over emphasized. We therefore propose two new loss functions
based on the Determinantal Point Process (DPP) likelihood, that can be
adaptively applied to estimate the subsequent item or items. The
DPP-distributed item set captures natural dependencies among temporal actions,
and a quality vs. diversity decomposition of the DPP kernel pushes us to go
beyond accuracy-oriented loss functions. Experimental results using the
proposed loss functions on three real-world datasets show marked improvements
over state-of-the-art sequential recommendation methods in both quality and
diversity metrics.
