Adversarial Personalized Ranking for Recommendation

Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) --- the most widely used model in recommendation --- as a demonstration, we show that optimizing it with BPR leads to a recommender model that is not robust. In particular, we find that the resultant model is highly vulnerable to adversarial perturbations on its model parameters, which implies the possibly large error in generalization. To enhance the robustness of a recommender model and thus improve its generalization performance, we propose a new optimization framework, namely Adversarial Personalized Ranking (APR). In short, our APR enhances the pairwise ranking method BPR by performing adversarial training. It can be interpreted as playing a minimax game, where the minimization of the BPR objective function meanwhile defends an adversary, which adds adversarial perturbations on model parameters to maximize the BPR objective function. To illustrate how it works, we implement APR on MF by adding adversarial perturbations on the embedding vectors of users and items. Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it outperforms BPR with a relative improvement of 11.2% on average and achieves state-of-the-art performance for item recommendation. Our implementation is available at: https://github.com/hexiangnan/adversarial_personalized_ranking.Comment: SIGIR 201

IntersectGAN: Learning Domain Intersection for Generating Images with Multiple Attributes

Generative adversarial networks (GANs) have demonstrated great success in generating various visual content. However, images generated by existing GANs are often of attributes (e.g., smiling expression) learned from one image domain. As a result, generating images of multiple attributes requires many real samples possessing multiple attributes which are very resource expensive to be collected. In this paper, we propose a novel GAN, namely IntersectGAN, to learn multiple attributes from different image domains through an intersecting architecture. For example, given two image domains $X_1$ and $X_2$ with certain attributes, the intersection $X_1 \cap X_2$ denotes a new domain where images possess the attributes from both $X_1$ and $X_2$ domains. The proposed IntersectGAN consists of two discriminators $D_1$ and $D_2$ to distinguish between generated and real samples of different domains, and three generators where the intersection generator is trained against both discriminators. And an overall adversarial loss function is defined over three generators. As a result, our proposed IntersectGAN can be trained on multiple domains of which each presents one specific attribute, and eventually eliminates the need of real sample images simultaneously possessing multiple attributes. By using the CelebFaces Attributes dataset, our proposed IntersectGAN is able to produce high quality face images possessing multiple attributes (e.g., a face with black hair and a smiling expression). Both qualitative and quantitative evaluations are conducted to compare our proposed IntersectGAN with other baseline methods. Besides, several different applications of IntersectGAN have been explored with promising results

The association of N-palmitoylethanolamine with the FAAH inhibitor URB597 impairs melanoma growth through a supra-additive action

<p>Abstract</p> <p>Background</p> <p>The incidence of melanoma is considerably increasing worldwide. Frequent failing of classical treatments led to development of novel therapeutic strategies aiming at managing advanced forms of this skin cancer. Additionally, the implication of the endocannabinoid system in malignancy is actively investigated.</p> <p>Methods</p> <p>We investigated the cytotoxicity of endocannabinoids and their hydrolysis inhibitors on the murine B16 melanoma cell line using a MTT test. Enzyme and receptor expression was measured by RT-PCR and enzymatic degradation of endocannabinoids using radiolabeled substrates. Cell death was assessed by Annexin-V/Propidium iodine staining. Tumors were induced in C57BL/6 mice by s.c. flank injection of B16 melanoma cells. Mice were injected i.p. for six days with vehicle or treatment, and tumor size was measured each day and weighted at the end of the treatment. Haematoxylin-Eosin staining and TUNEL assay were performed to quantify necrosis and apoptosis in the tumor and endocannabinoid levels were quantified by HPLC-MS. Tube formation assay and CD31 immunostaining were used to evaluate the antiangiogenic effects of the treatments.</p> <p>Results</p> <p>The <it>N</it>-arachidonoylethanolamine (anandamide, AEA), 2-arachidonoylglycerol and <it>N</it>- palmitoylethanolamine (PEA) reduced viability of B16 cells. The association of PEA with the fatty acid amide hydrolase (FAAH) inhibitor URB597 considerably reduced cell viability consequently to an inhibition of PEA hydrolysis and an increase of PEA levels. The increase of cell death observed with this combination of molecules was confirmed in vivo where only co-treatment with both PEA and URB597 led to decreased melanoma progression. The antiproliferative action of the treatment was associated with an elevation of PEA levels and larger necrotic regions in the tumor.</p> <p>Conclusions</p> <p>This study suggests the interest of targeting the endocannabinoid system in the management of skin cancer and underlines the advantage of associating endocannabinoids with enzymatic hydrolysis inhibitors. This may contribute to the improvement of long-term palliation or cure of melanoma.</p