How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}

A unified approach to collaborative data visualization

Much efforts have lately been concentrated on increasing the precision of recommendations following the Netflix Prize competition. Recently, many researchers and industries have noted that other factors like adequate presentation of the results can add more utility to a recommender system than slight improvement in the precision. In this paper, we suggest a methodology for user-friendly representation of recommendations to the end users. Our scheme unifies the two objectives of prediction and visualization in the core of a unique approach. Users and items are first embedded into a high dimensional latent feature space according to a predictor function, particularly designated to meet visualization requirements. The data is then projected into a $2$-dimensional space by Curvilinear Component Analysis (CCA). CCA draws personalized Item Maps (PIMs) representing a small subset of items to the active user. The intra-item semantic correlations are preserved in PIMs which is inherited from the clustering property of the high-dimensional embedding space. Our prediction function and the projection method are both non-linear to increase the clarity of the maps and to limit the effect of projection error. The algorithms are tested on three versions of the MovieLens dataset and the Netflix dataset to show they combine good accuracy with satisfactory visual properties