Negative-Aware Collaborative Filtering

Most traditional recommender systems regard unseen user-item associations as negative user preferences and optimize recommendation models mainly based on observed associations and some negative instances sampled from unseen associations. However, such unseen user-item associations may contain potential positive user preferences on items and are not uniformly distributed in terms of the possibility of being negative (or positive) user preference; therefore, it is essential to quantify such associations for model training. Along this line, in this paper, in contrast to existing recommendation models, which equally treat all unseen associations as negative samples, we present a negative-aware recommendation approach that explicitly models the likelihood of each unseen association being a potentially positive preference. Empirical results on real-world datasets in different fields show that our approach consistently improves recommendation performance.