  Implicit feedback has been widely used to build commercial recommender
systems. Because observed feedback represents users' click logs, there is a
semantic gap between true relevance and observed feedback. More importantly,
observed feedback is usually biased towards popular items, thereby
overestimating the actual relevance of popular items. Although existing studies
have developed unbiased learning methods using inverse propensity weighting
(IPW) or causal reasoning, they solely focus on eliminating the popularity bias
of items. In this paper, we propose a novel unbiased recommender learning
model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the
exposure bias of items caused by recommender models. Specifically, BISER
consists of two key components: (i) self-inverse propensity weighting (SIPW) to
gradually mitigate the bias of items without incurring high computational
costs; and (ii) bilateral unbiased learning (BU) to bridge the gap between two
complementary models in model predictions, i.e., user- and item-based
autoencoders, alleviating the high variance of SIPW. Extensive experiments show
that BISER consistently outperforms state-of-the-art unbiased recommender
models over several datasets, including Coat, Yahoo! R3, MovieLens, and
CiteULike.
