Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data

Most sequential recommender systems (SRSs) predict next-item as target for each user given its preceding items as input, assuming that each input is related to its target. However, users may unintentionally click on items that are inconsistent with their preference. We empirically verify that SRSs can be misguided with such unreliable instances (i.e. targets mismatch inputs). This inspires us to design a novel SRS By Eliminating unReliable Data (BERD) guided with two observations: (1) unreliable instances generally have high training loss; and (2) high-loss instances are not necessarily unreliable but uncertain ones caused by blurry sequential pattern. Accordingly, BERD models both loss and uncertainty of each instance via a Gaussian distribution to better distinguish unreliable instances; meanwhile an uncertainty-aware graph convolution network is exploited to assist in mining unreliable instances by lowering uncertainty. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed BERD.