  In this work, we propose FM-Pair, an adaptation of Factorization Machines
with a pairwise loss function, making them effective for datasets with implicit
feedback. The optimization model in FM-Pair is based on the BPR (Bayesian
Personalized Ranking) criterion, which is a well-established pairwise
optimization model. FM-Pair retains the advantages of FMs on generality,
expressiveness and performance and yet it can be used for datasets with
implicit feedback. We also propose how to apply FM-Pair effectively on two
collaborative filtering problems, namely, context-aware recommendation and
cross-domain collaborative filtering. By performing experiments on different
datasets with explicit or implicit feedback we empirically show that in most of
the tested datasets, FM-Pair beats state-of-the-art learning-to-rank methods
such as BPR-MF (BPR with Matrix Factorization model). We also show that FM-Pair
is significantly more effective for ranking, compared to the standard FMs
model. Moreover, we show that FM-Pair can utilize context or cross-domain
information effectively as the accuracy of recommendations would always improve
with the right auxiliary features. Finally we show that FM-Pair has a linear
time complexity and scales linearly by exploiting additional features.
