  Nowadays, recommender systems already impact almost every facet of peoples
lives. To provide personalized high quality recommendation results,
conventional systems usually train pointwise rankers to predict the absolute
value of objectives and leverage a distinct shallow tower to estimate and
alleviate the impact of position bias. However, with such a training paradigm,
the optimization target differs a lot from the ranking metrics valuing the
relative order of top ranked items rather than the prediction precision of each
item. Moreover, as the existing system tends to recommend more relevant items
at higher positions, it is difficult for the shallow tower based methods to
precisely attribute the user feedback to the impact of position or relevance.
Therefore, there exists an exciting opportunity for us to get enhanced
performance if we manage to solve the aforementioned issues. Unbiased learning
to rank algorithms, which are verified to model the relative relevance
accurately based on noisy feedback, are appealing candidates and have already
been applied in many applications with single categorical labels, such as user
click signals. Nevertheless, the existing unbiased LTR methods cannot properly
handle multiple feedback incorporating both categorical and continuous labels.
Accordingly, we design a novel unbiased LTR algorithm to tackle the challenges,
which innovatively models position bias in the pairwise fashion and introduces
the pairwise trust bias to separate the position bias, trust bias, and user
relevance explicitly. Experiment results on public benchmark datasets and
internal live traffic show the superior results of the proposed method for both
categorical and continuous labels.
