Users’ feedback information as the ground-truth has attracted a lot of attention in recommender systems. However, the feedback that could be contaminated by users’ misoperations or malicious operations is probably not true in real scenarios. This work aims to develop a technique based on an improved Bayesian personalized ranking (BPR), called adversarial training-based mean Bayesian personalized ranking (AT-MBPR). In this method, we divide the feedback information into three categories based on the mean Bayesian personalized ranking (MBPR), then we gain the implicit feedback from the mean and non-observed items of each user, following which, adversarial perturbations are added on the embedding vectors of the users and items by playing a minimax game to reduce the noise. The experiments demonstrate in five datasets that our approach outperforms the traditional BPR methods and state-of-the-art methods used for the recommendation. Our implementation is available at: https://github.com/ HanXia001/ Adversarial -Training-based-Mean-BPR-for-Recommender.

Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification method is investigated by flexible patches sampling of superpixels. First, the principal component analysis and total variation diffusion are employed to form the pseudo color image for simplifying superpixels computing with (simple linear iterative clustering) SLIC model. Then, we design a joint sparse recovery model by sampling overcomplete patches of superpixels to estimate joint sparse characteristics of test pixel, which are carried out on the orthogonal matching pursuit (OMP) algorithm. At last, the pixel is labeled according to the minimum distance constraint for final classification based on the joint sparse coefficients and structured dictionary. Experiments conducted on two real hyperspectral datasets show the superiority and effectiveness of the proposed method.

Personalized recommendation systems predict potential demand by analyzing user preferences. Generally, user feedback information is inferred from implicit feedback or explicit feedback. Nevertheless, feedback can be contaminated by user's mis-operations or malicious operations, and may thus lead to incorrect results. We propose a novel Multi-feedback pairwise ranking method via Adversarial training (AT-MPR) for recommender to enhance the robustness and overall performance in the event of rating pollution. The MPR method extends Bayesian personalized ranking (BPR) to cover three types of feedback: positive, negative, and unobserved. It obtains user preferences in a probabilistic way through multiple feedbacks at different levels. To reduce the impact of feedback noise, we train an MPR objective function using minimax adversarial training. Experiments on two datasets show that the AT-MPR model achieves satisfactory performance and outperforms the state-of-the-art implicit feedback collaborative ranking models in two evaluation metrics.