1,720,981 research outputs found
Semantics-Aware Autoencoder
Full text linkRecommender Systems are widely adopted nowadays in many services, such as e-commerce websites, content streaming platforms for both music, videos, or just news. They aim to help users to find what they look for by filtering only the relevant content to them in a personalized fashion since every user has its tastes.
Over the years, several algorithms have been developed to solve the recommendation problem. Very recently, we assisted in the rise of Deep Learning, which had been able to outperform many state-of-the-art machine learning algorithms. On the other hand, even though deep learning is very effective for this problem, it is hard to explain as the model is not interpretable.
In this thesis, we present SemAuto, a novel deep learning interpretable architecture that can explain its outputs, and that can be used to generate an explanation for the provided recommendation. We evaluated our semantics-aware approach with respect to other state-of-the-art algorithms to prove the recommendation's accuracy effectiveness. Furthermore, we performed an extensive A/B test with real users to evaluate the explanation we generate
LEARNING ON GRAPHS: ALGORITHMS FOR CLASSIFICATION AND SEQUENTIAL DECISIONS
Full text linkIn recent years, networked data have become widespread due to the increasing importance of social networks and other web-related applications. This growing interest is driving researchers to design new algorithms for solving important problems that involve networked data.
In this thesis we present a few practical yet principled algorithms for learning and sequential decision-making on graphs.
Classification of networked data is an important problem that has recently received a great deal of attention from the machine learning community. This is due to its many important practical applications: computer vision, bioinformatics, spam detection and text categorization, just to cite a few of the more conspicuous examples.
We focus our attention on the task called ``node classification'', often studied in the semi-supervised (transductive) setting. We present two algorithms, motivated by different theoretical frameworks. The first algorithm is studied in the well-known online adversarial setting, within which it enjoys an optimal mistake bound (up to logarithmic factors). The second algorithm is based on a game-theoretic approach, where each node of the network is maximizing its own payoff. The setting corresponds to a Graph Transduction Game in which the graph is a tree. For this special case, we show that the Nash Equilibrium of the game can be reached in linear time.
We complement our theoretical findings with an extensive set of experiments using datasets from many different domains.
In the second part of the thesis, we present a rapidly emerging theme in the analysis of networked data: signed networks, graphs whose edges carry a label encoding the positive or negative nature of the relationship between the connected nodes. For example, social networks and e-commerce offer several examples of signed relationships: Slashdot users can tag other users as friends or foes, Epinions users can rate each other positively or negatively, Ebay users develop trust and distrust towards sellers in the network. More generally, two individuals that are related because they rate similar products in a recommendation website may agree or disagree in their ratings.
Many heuristics for link classification in social networks are based on a form of social balance summarized by the motto “the enemy of my enemy is my friend”. This is equivalent to saying that the signs on the edges of a social graph tend to be consistent with some two-clustering structure of the nodes, where edges connecting nodes from the same cluster are positive and edges connecting nodes from different clusters are negative.
We present algorithms for the batch transductive active learning setting, where the topology of the graph is known in advance and our algorithms can ask for the label of some specific edges during the training phase (before starting with the predictions).
These algorithms can achieve different tradeoffs between the number of mistakes during the test phase and the number of labels required during the training phase.
We also presented an experimental comparison against some state-of-the-art spectral heuristics presented in a previous work, where we show that the simplest or our algorithms is already competitive with the best of these heuristics.
In the last chapter we present another way to exploit relational information for sequential predictions: the networks of bandits.
Contextual bandits adequately formalize the exploration-exploitation trade-offs arising in several industrially relevant applications, such online advertisement and recommendation systems.
Many practical applications have a strong social component whose integration in the bandit algorithm could lead to a significant performance improvement: for example, since often friends have similar taste, we may want to serve contents to a group of users by taking advantage of an underlying network of social relationships among them.
We introduce a novel algorithmic approach to a particular networked bandit problem. More specifically, we run a bandit algorithm on each network node (e.g., user), allowing it to ``share'' feedback signals with the other nodes by employing the multi-task kernel.
We derive the regret analysis of this algorithm and, finally, we report on the results of an experimental comparison between our approach and the state of the art techniques, on both artificial and real-world social networks
Random spanning trees and the prediction of weighted graphs
No full textWe show that the mistake bound for predicting the nodes of an arbitrary weighted graph is characterized (up to logarithmic factors) by the cutsize of a random spanning tree of the graph. The cutsize is induced by the unknown adversarial labeling of the graph nodes. In deriving our characterization, we obtain a simple randomized algorithm achieving the optimal mistake bound on any weighted graph. Our algorithm draws a random spanning tree of the original graph and then predicts the nodes of this tree in constant amortized time and linear space. Experiments on real-world datasets show that our method compares well to both global (Perceptron) and local (label-propagation) methods, while being much faster. Copyright 2010 by the author(s)/owner(s)
Cavalcalupo, Girolamo
No full textLa voce dedicata a Girolamo Cavalcalupo in "Dizionario dei tipografi degli editori italiani. Il Cinquecento. Vol. 1. A-F" a cura di Marco Menato, Ennio Sandal, Giuseppina Zappella. Milano, Editrice bibliografica, 199
Cavalcalupo, Domenico
No full textLa voce dedicata a Domenico Cavalcalupo in "Dizionario dei tipografi degli editori italiani. Il Cinquecento. Vol. 1. A-F" a cura di Marco Menato, Ennio Sandal, Giuseppina Zappella. Milano, Editrice bibliografica, 199
Active learning on trees and graphs
No full textWe investigate the problem of active learning on a given tree whose nodes are assigned binary labels in an adversarial way. Inspired by recent results by Guillory and Bilmes, we characterize(up to constant factors) the optimal placement of queries so to minimize the mistakes made on the non-queried nodes. Our query selection algorithm is extremely efficient, and the optimal number of mistakes on the non-queried nodes is achieved by a simple and efficient mincut classifier. Through a simple modification of the query selection algorithm we also show optimality (up to constant factors) with respect to the trade-off between number of queries and number of mistakes on nonqueried nodes. By using spanning trees, our algorithms can be efficiently applied to general graphs, although the problem of finding optimal and efficient active learning algorithms for general graphs remains open. Towards this end, we provide a lower bound on the number of mistakes made on arbitrary graphs by any active learning algorithm using a number of queries which is up to a
constant fraction of the graph siz
Ferrari, Giovanni Maria e Giacomo
No full textLa voce dedicata a Giovanni Maria e Giacomo Ferrari in "Dizionario dei tipografi degli editori italiani. Il Cinquecento. Vol. 1. A-F" a cura di Marco Menato, Ennio Sandal, Giuseppina Zappella. Milano, Editrice bibliografica, 199
A correlation clustering approach to link classification in signed networks
Full text linkMotivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our main algorithmic contribution is in the active setting, where we introduce a new family of efficient link classifiers based on covering the input graph with small circuits. These are the first active algorithms for link classification with mistake bounds that hold for arbitrary signed networks
Calvo, Andrea
No full textLa voce dedicata ad Andrea Calvo in "Dizionario dei tipografi degli editori italiani. Il Cinquecento. Vol. 1. A-F" a cura di Marco Menato, Ennio Sandal, Giuseppina Zappella. Milano, Editrice bibliografica, 199
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