INVERSE LEARNING WITH EXTREMELY SPARSE FEED-BACK FOR RECOMMENDATION

Abstract

Negative sampling is widely used in modern recommender systems, where negative data is randomly sampled from the whole item pool. However, such a strategy often introduces false-negative noises. Existing approaches about de-noising recommendation mainly focus on positive instances while ignoring the noise in the large amount of sampled negative feedback. In this paper, we propose a meta learning method to annotate the unlabeled data from loss and gradient perspectives, which considers the noises on both positive and negative instances. Specifically, we first propose inverse dual loss (IDL) to boost the true label learning and prevent the false label learning, based on the loss of unlabeled data towards true and false labels during the training process. To achieve more robust sampling on hard instances, we further propose inverse gradient (IG) to explore the correct updating gradient and adjust the updating based on meta learning. We conduct extensive experiments on a benchmark and an industrially collected dataset where our proposed method can significantly improve AUC by 9.25% against state-of-the-art methods. Further analysis verifies the proposed inverse learning is model-agnostic and can well annotate the labels combined with different recommendation backbones. The source code along with the best hyper-parameter settings is available at this link:

1. INTRODUCTION

As one of the most successful machine learning applications in industry, recommender systems are essential to promote user experience and improve user engagement (Ricci et al., 2011; Xue et al., 2017; Liu et al., 2010b) , which are widely adopted in online services such as E-commerce and Mirco-video platforms. Aiming to capture users' preference towards items based on their historical behaviors, existing recommenders generally focus on explicit or implicit feedback. Specifically, explicit feedback (Liang et al., 2021) refers to rating data that represents the user preference explicitly. However, collecting sufficient explicit data for recommendations is difficult because it requires users to actively provide ratings (Jannach et al., 2018) . In contrast, implicit feedback, such as user clicks, purchases, and views (Liang et al., 2016) , is much richer (Liu et al., 2010b) and frequently used in modern recommender systems (Chen et al., 2020) . Particularly, in feed recommendation for online platforms such as Micro-video, users are passive to receive the recommended items without any active clicking or rating action. That is to say, we have a large number of unlabeled feedback, with extremely sparse labeled data, which becomes a key challenge for recommendation. To tackle the unlabeled feedback, most works (He et al., 2017; Chen et al., 2019) randomly sample unlabeled data and treat it as negative feedback, resulting in unavoidable noise. Specifically, the collected user click data is often treated as positive feedback, and the unclicked data is sampled as negative feedback (He et al., 2017; Chen et al., 2019) . However, there may be some positive unlabeled data sampled by the negative sampling strategy, which means that these instances will be false-negative. There are also some works about hard negative sampling which will decrease the false-positive but increase the false-negative instances (Zhang et al., 2013; Ding et al., 2019; 2020) . These hard negative methods tend to perform poorly when tested on both true positive and negative data instead of true positive but sampled negative data (as shown in Appendix 5). A recent work DenoisingRec (Wang et al., 2021) denoises positive feedback by truncating or reweighing the loss of false-positive instances without further consideration of the noisy negative feedback. In general,

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