Recently, substantial progress has been made in the field of modeling sequential recommendation tasks through the application of deep neural networks. However, practical implementations of sequential deep learning models have been shown to be prone to representation degradation problems, which may leading to high semantic similarities among embeddings, and severely impact the recommendation performance and robustness. For alleviating this problem, in this paper, we propose a simple yet highly effective Adversarial Training mechanism for regularizing Sequential Recommendation models, namely ATSRec. In specific, we first conduct an empirical and theoretical study of this representation degradation problem. Then, we introduce adversarial perturbations to the item embedding layer, aiming to maximize the adversarial loss during model training. At last, theoretically, we show that our adversarial mechanism effectively encourages the diversity of the embedding vectors, helping to increase the robustness of models. Through extensive experiments on four public datasets and seven state-of-the-art models, we observed substantial improvements in both model overall performance and robustness with the help of ATSRec.

Contrastive learning has recently emerged as an effective strategy for improving the performance of sequential recommendation. However, traditional models commonly construct the contrastive loss by directly optimizing human-designed positive and negative samples, resulting in a model that is overly sensitive to heuristic rules. To address this limitation, we propose a novel soft contrastive framework for sequential recommendation in this article. Our main idea is to extend the point-wise contrast to a region-level comparison, where we aim to identify instances near the initially selected positive/negative samples that exhibit similar contrastive properties. This extension improves the model’s robustness to human heuristics. To achieve this objective, we introduce an adversarial contrastive loss that allows us to explore the sample regions more effectively. Specifically, we begin by considering the user behavior sequence as a holistic entity. We construct adversarial samples by introducing a continuous perturbation vector to the sequence representation. This perturbation vector adds variability to the sequence, enabling more flexible exploration of the sample regions. Moreover, we extend the aforementioned strategy by applying perturbations directly to the items within the sequence. This accounts for the sequential nature of the items. To capture these sequential relationships, we utilize a recurrent neural network to associate the perturbations, which introduces an inductive bias for more efficient exploration of adversarial samples. To demonstrate the effectiveness of our model, we conduct extensive experiments on five real-world datasets.

With the ever prospering of the web technologies, there is a common need to make recommendations from heterogeneous sources, such as recommending products and advertisements together on the e-commerce websites. People usually solve such recommendation problem by a two-stage paradigm, where the first stage is generating candidates from each source, and the second one is aggregating and ranking the generated heterogeneous candidates to produce the final results. While existing models have achieved many successes, they mostly optimize the above two stages separately, where the user preferences can only be used to supervise the second stage, while for the first one, there is no signal to tell whether the generated candidates are accurate enough to cover the user preference. To solve the above problem, in this paper, we design a multi-agent framework to jointly optimize the above two stages. In specific, suppose there are N sources in our problem, then we deploy N+1 agents, where the first N agents correspond one-to-one with the sources, aiming to select the sources-specific candidates, and the last agent is designed to aggregate the candidates from different sources for the final recommendation. All the agents play a cooperative game, aiming to maximize the rewards revealing user preferences. We implement our idea based on the Deep Q-network, where we design a decomposable reward to enhance the training efficiency. We adapt our model to a real-world recommendation problem abstracted from a famous short video platform-Kuaishou.com. We conduct extensive experiments to demonstrate the effectiveness of our model.

Inaccurate-supervised learning (ISL) is a weakly supervised learning framework for imprecise annotation, which is derived from some specific popular learning frameworks, mainly including partial label learning (PLL), partial multilabel learning (PML), and multiview PML (MVPML). While PLL, PML, and MVPML are each solved as independent models through different methods and no general framework can currently be applied to these frameworks, most existing methods for solving them were designed based on traditional machine-learning techniques, such as logistic regression, KNN, SVM, decision tree. Prior to this study, there was no single general framework that used adversarial networks to solve ISL problems. To narrow this gap, this study proposed an adversarial network structure to solve ISL problems, called ISL with generative adversarial nets (ISL-GANs). In ISL-GAN, fake samples, which are quite similar to real samples, gradually promote the Discriminator to disambiguate the noise labels of real samples. We also provide theoretical analyses for ISL-GAN in effectively handling ISL data. In this article, we propose a general framework to solve PLL, PML, and MVPML, while in the published conference version, we adopt the specific framework, which is a special case of the general one, to solve the PLL problem. Finally, the effectiveness is demonstrated through extensive experiments on various imprecise annotation learning tasks, including PLL, PML, and MVPML.

Partial label learning (PLL) is to learn a discriminative model under incomplete supervision, where each instance is annotated with a candidate label set. The basic principle of PLL is that the unknown correct label y of an instance x resides in its candidate label set s, i.e., P(y ∈ s | x) = 1. On which basis, current researches either directly model P(x | y) under different data generation assumptions or propose various surrogate multiclass losses, which all aim to encourage the model-based Pθ(y ∈ s | x)→1 implicitly. In this work, instead, we explicitly construct a binary classification task toward P(y ∈ s | x) based on the discriminative model, that is to predict whether the model-output label of x is one of its candidate labels. We formulate a novel risk estimator with estimation error bound for the proposed PLL binary classification risk. By applying logit adjustment based on disambiguation strategy, the practical approach directly maximizes Pθ(y ∈ s | x) while implicitly disambiguating the correct one from candidate labels simultaneously. Thorough experiments validate that the proposed approach achieves competitive performance against the state-of-the-art PLL methods.