Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement

One-hot encoder accompanied by a softmax loss has become the default configuration to deal with the multiclass problem, and is also prevalent in deep learning (DL) based recommender systems (RS). The standard learning process of such methods is to fit the model outputs to a one-hot encoding of the ground truth, referred to as the hard target. However, it is known that these hard targets largely ignore the ambiguity of unobserved feedback in RS, and thus may lead to sub-optimal generalization performance. In this work, we propose SoftRec, a new RS optimization framework to enhance item recommendation. The core idea is that we add additional supervisory signals - well-designed soft targets - for each instance so as to better guide the recommender learning. Meanwhile, we carefully investigate the impacts of specific soft target distributions by instantiating the SoftRec with a series of strategies, including item-based, user-based, and model-based. To verify the effectiveness of SoftRec, we conduct extensive experiments on two public recommendation datasets by using various deep recommendation architectures. The experimental results show that our methods achieve superior performance compared with the standard optimization approaches. Moreover, SoftRec could also exhibit strong performance in cold-start scenarios where user-item interaction has higher sparsity.