Generative Adversarial Networks (GAN) have achieved big success in various domains such as image generation, music generation, and natural language generation. In this paper, we propose a novel GAN-based collaborative filtering (CF) framework to provide higher accuracy in recommendation. We first identify a fundamental problem of existing GAN-based methods in CF and highlight it quantitatively via a series of experiments. Next, we suggest a new direction of vector-wise adversarial training to solve the problem and propose our GAN-based CF framework, called CFGAN, based on the direction. We identify a unique challenge that arises when vector-wise adversarial training is employed in CF. We then propose three CF methods realized on top of our CFGAN that are able to address the challenge. Finally, via extensive experiments on real-world datasets, we validate that vector-wise adversarial training employed in CFGAN is really effective to solve the problem of existing GAN-based CF methods. Furthermore, we demonstrate that our proposed CF methods on CFGAN provide recommendation accuracy consistently and universally higher than those of the state-of-the-art recommenders.

CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks

For evaluating machine-generated texts, automatic methods hold the promise of avoiding collection of human judgments, which can be expensive and time-consuming. The most common automatic metrics, like BLEU and ROUGE, depend on exact word matching, an inflexible approach for measuring semantic similarity. We introduce methods based on sentence mover’s similarity; our automatic metrics evaluate text in a continuous space using word and sentence embeddings. We find that sentence-based metrics correlate with human judgments significantly better than ROUGE, both on machine-generated summaries (average length of 3.4 sentences) and human-authored essays (average length of 7.5). We also show that sentence mover’s similarity can be used as a reward when learning a generation model via reinforcement learning; we present both automatic and human evaluations of summaries learned in this way, finding that our approach outperforms ROUGE.

Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts

We propose a new model toward improving the quality of image recommendations in social sharing communities like Pinterest, Flickr, and Instagram. Concretely, we propose Neural Personalized Ranking (NPR) -- a personalized pairwise ranking model over implicit feedback datasets -- that is inspired by Bayesian Personalized Ranking (BPR) and recent advances in neural networks. We further build an enhanced model by augmenting the basic NPR model with multiple contextual preference clues including user tags, geographic features, and visual factors. In our experiments over the Flickr YFCC100M dataset, we demonstrate the proposed NPR model is more effective than multiple baselines. Moreover, the contextual enhanced NPR model significantly outperforms the base model by 16.6% and a contextual enhanced BPR model by 4.5% in precision and recall.

https://dl.acm.org/doi/pdf/10.1145/3159652.3159728

Neural Personalized Ranking for Image Recommendation

With the exponential development of the number of users browsing the internet, an important factor that now the developer community is focussing on is the user experience Recommender systems are the platforms that make personalized recommendations for a particular user by predicting the ratings for various items Recommender systems majorly ignore the sequential information and rather focus on content information, but sequential information also provides much information about the behavior of the user In this research work, we have presented a novel web-based recommender system which is based on sequential information of user’s navigation on web pages We received top-N clusters when Fuzzy C-mean (FCM) clustering is employed We determined the similar users for the target user and also evaluated the weight for each web page We have tried to solve that problem of recommender systems as we offered a system to forecast a user’s next Web page visit In our work, we proposed a system which generates recommendations to the users, by considering the sequential information that exists in their usage patterns of Web pages We employed fuzzy clustering to give recommender system a sequential approach We calculated weights for each page category considered in our system and predict top page recommendation for the target user The real-world dataset of MNSBC is used in the experiments The dataset consists of 5000 user entries with 6, entries per user When we performed a comparison between the existing model with our proposed model, then it clearly showed that the accuracy of the proposed model is almost three times better than some existing systems The accuracy of our proposed model is nearly 33 %

An effective web page recommender system with fuzzy c-mean clustering

We derive limit distributions for certain empirical regularized optimal transport distances between probability distributions supported on a finite metric space and show consistency of the (naive) bootstrap. In particular, we prove that the empirical regularized transport plan itself asymptotically follows a Gaussian law. The theory includes the Boltzmann-Shannon entropy regularization and hence a limit law for the widely applied Sinkhorn divergence. Our approach is based on an application of the implicit function theorem to necessary and sufficient optimality conditions for the regularized transport problem. The asymptotic results are investigated in Monte Carlo simulations. We further discuss computational and statistical applications, e.g. confidence bands for colocalization analysis of protein interaction networks based on regularized optimal transport.

Empirical Regularized Optimal Transport: Statistical Theory and Applications

In this paper, we describe a novel, publicly available collection for recommendation systems that records the behavior of customers of the European leader in eCommerce advertising, Kelkoo\footnote{\url{https://www.kelkoo.com/}}, during one month. This dataset gathers implicit feedback, in form of clicks, of users that have interacted with over 56 million offers displayed by Kelkoo, along with a rich set of contextual features regarding both customers and offers. In conjunction with a detailed description of the dataset, we show the performance of six state-of-the-art recommender models and raise some questions on how to encompass the existing contextual information in the system.

KASANDR: A Large-Scale Dataset with Implicit Feedback for Recommendation

Machine learning and data mining algorithms have been increasingly used recently to support decision-making systems in many areas of high societal importance such as healthcare, education, or security. While being very efficient in their predictive abilities, the deployed algorithms sometimes tend to learn an inductive model with a discriminative bias due to the presence of this latter in the learning sample. This problem gave rise to a new field of algorithmic fairness where the goal is to correct the discriminative bias introduced by a certain attribute in order to decorrelate it from the model's output. In this paper, we study the problem of fairness for the task of edge prediction in graphs, a largely underinvestigated scenario compared to a more popular setting of fair classification. To this end, we formulate the problem of fair edge prediction, analyze it theoretically, and propose an embedding-agnostic repairing procedure for the adjacency matrix of an arbitrary graph with a trade-off between the group and individual fairness. We experimentally show the versatility of our approach and its capacity to provide explicit control over different notions of fairness and prediction accuracy.

All of the Fairness for Edge Prediction with Optimal Transport.

Hard and fuzzy diagonal co-clustering for document-term partitioning

In this paper, we present a novel and publicly available dataset for online recommendation provided by Purch1. The dataset records the clicks generated by users of one of Purch's high-tech website over the ads they have been shown for one month. In addition, the dataset contains contextual information about offers such as offer titles and keywords, as well as the anonymized content of the page on which offers were displayed. Then, besides a detailed description of the dataset, we evaluate the performance of six popular baselines and propose a simple yet effective strategy on how to overcome the existing challenges inherent to implicit feedback and popularity bias introduced while designing an efficient and scalable recommendation algorithm. More specifically, we propose to demonstrate the importance of introducing diversity based on an appropriate representation of items in Recommender Systems, when the available feedback is strongly biased.

Learning to recommend diverse items over implicit feedback on PANDOR

In this paper, we present a novel method for co-clustering, an unsupervised learning approach that aims at discovering homogeneous groups of data instances and features by grouping them simultaneously The proposed method uses the entropy regularized optimal transport between empirical measures defined on data instances and features in order to obtain an estimated joint probability density function represented by the optimal coupling matrix This matrix is further factorized to obtain the induced row and columns partitions using multiscale representations approach To justify our method theoretically, we show how the solution of the regularized optimal transport can be seen from the variational inference perspective thus motivating its use for co-clustering The algorithm derived for the proposed method and its kernelized version based on the notion of Gromov-Wasserstein distance are fast, accurate and can determine automatically the number of both row and column clusters These features are vividly demonstrated through extensive experimental evaluations

Charlotte Laclau

Papers

KASANDR: A Large-Scale Dataset with Implicit Feedback for Recommendation

All of the Fairness for Edge Prediction with Optimal Transport.

Hard and fuzzy diagonal co-clustering for document-term partitioning

Learning to recommend diverse items over implicit feedback on PANDOR

Co-clustering through Optimal Transport