Negative sampling plays an important role in ranking-based recommender models. However, most existing sampling methods cannot generate informative item pairs with positive and negative instances due to two limitations: 1) they merely treat observed items as positive instances, ignoring the existence of potential positive items (i.e., nonobserved items users may prefer) and the probability of observed but noisy items and 2) they fail to capture the relationship between positive and negative items during negative sampling, which may cause the unexpected selection of potential positive items. In this article, we introduce a dynamic sampling strategy to search informative item pairs. Specifically, we first sample a positive instance from all the items by leveraging the overall features of user's observed items. Then, we strategically select a negative instance by considering its correlation with the sampled positive one. Formally, we propose an item pair generative adversarial network named IPGAN, where our sampling strategy is realized in two generative models for positive and negative instances, respectively. In addition, IPGAN can also ensure that the sampled item pairs are informative relative to the ground truth by a discriminative model. What is more, we propose a batch-training approach to further enhance both user and item modeling by alleviating the special bias (noise) from different users. This approach can also significantly accelerate the process of model training compared with classical GAN method for recommendation. Experimental results on three real data sets show that our approach outperforms other state-of-the-art approaches in terms of recommendation accuracy.

We describe a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. Our system is weakly supervised by automatically generated semantic maps (with floor, wall, ceiling, lamp, window and door labels) that have shown success on perspective views and are trained for panoramas using transfer learning without any further annotations. A GAN-based approach supervised by coarse information obtained from the semantic map extracts specular reflection and direct sunlight regions on the floor and walls. These lighting effects are removed via a similar GAN-based approach and a semantic-aware inpainting step. The appearance decomposition enables multiple applications including sun direction estimation, virtual furniture insertion, floor material replacement, and sun direction change, providing an effective tool for virtual home staging. We demonstrate the effectiveness of our approach on a large and recently released dataset of panoramas of empty homes.

Paper recommendation is a research topic to provide users with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring their semantic relationship. In this paper, we regard the abstract as a sequence of sentences, and propose a two-level attentive neural network to capture: (1) the ability of each word within a sentence to reflect if it is semantically close to the words within the title. (2) the extent of each sentence in the abstract relative to the title, which is often a good summarization of the abstract document. Specifically, we propose a Long-Short Term Memory (LSTM) network with attention to learn the representation of sentences, and integrate a Gated Recurrent Unit (GRU) network with a memory network to learn the long-term sequential sentence patterns of interacted papers for both user and item (paper) modeling. We conduct extensive experiments on two real datasets, and show that our approach outperforms other state-of-the-art approaches in terms of accuracy.

Recently, GAN-based collaborative filtering methods have gained increasing attention in recommendation tasks which can learn remarkable user and item representation. However, these existing GAN-based methods mainly suffer from two limitations: (1) Their trainings are not comprehensive given the fact that the discriminator may be trained misleadingly and over-early converging since the generator may accidentally sample real items as fake ones, resulting in the emergence of contradicting labels for the same items. (2) They fail to consider implicit friends (users with the same interests.), leading to severe limitations of recommendation performance. In this paper, we propose BiGAN, an innovative bidirectional adversarial recommendation model which can alleviate the limitations mentioned above in recommendation tasks. It consists of two GANs, namely ForwardGAN and BackwardGAN. Specifically, ForwardGAN learns to generate a group of possible interacted items given a specific user, it aims to ensure that the discriminator Df can be trained effectively. Furthermore, BackwardGAN fully exploits implicit friends with similar behaviors, then propagates them back to ForwardGAN, where a similarity exploration strategy is implemented to gain more outstanding user representation. Therefore, two GANs are trained jointly in a circle, where the augment of one GAN will enhance another one, leading to the promising user and item representation. In the experimental part, we demonstrate that our model is superior to other state-of-the-art recommenders.

In this paper, we propose a new factorization model that combines multi-view visual feature information with the implicit feedback data for prediction and ranking. The visual information is integrated into a collaborative filtering framework. The visual features of images are extracted by using a deep neural network. In order to conduct personalized recommendation better, the multi-view visual features are fused through user related weights. The user related weights reflect the personalized visual preference for items. They are different and independent between users. Experimental results show that our model with multi-view visual information achieves the better performance than models without or with only single-view visual information.