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Individual investors were denied access to them over the counter . Yes an elite group of traders used the futures exchange to profit from Binary Options . Just a few years ago Binary Options which were formally know as exotic Options were the preserve of the select few . Fees were all paid in advance in order to buy Binary Options on the exchange . In 2008 however the curtain was drawn back on Binary Options , allowing regular people trading access. It was the Options Clearing Corporation began listing binary options over the counter that began to open the doors. You see they look at the risk verse reward and come to the conclusion the rewards are greater in trading the financial instruments. The American Stock Exchange also played a very important part in allowing people to trade binary Option online. Binary Options are in great demand in 2012 , some say it because of the lucrative nature of the Fixed odds . Now just like Forex and stock you can sit at home and trade Binary Options 24/6. Those in the know hired the services of special brokers in order to trade Binary options . Of course the increase in Binary Option Brokers offer a more direct options trading is another reason why the movement is towards Binary Options .

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Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns joint convolutional representations from a nearest neighbor and a furthest neighbor graph to establish a novel accuracy-diversity trade-off for recommender systems. Compared with accuracy-oriented graph convolutional approaches, Binary Options the proposed model shows diversity gains up to seven times by trading as little as 1 % in accuracy. The information between the two convolutional modules is balanced already in the training phase through a regularizer inspired by multi-kernel learning. To our knowledge, this is the first work proposing an accuracy-diversity trade-off with graph convolutions and opens the doors to learning over graphs approaches for improving such trade-off. The nearest neighbor graph connects entities (users or items) based on their similarities and is responsible for improving accuracy, while the furthest neighbor graph connects entities based on their dissimilarities and is responsible for diversifying recommendations. We evaluate the joint convolutional model on three benchmark datasets with different degrees of sparsity. Compared with alternative accuracy-diversity trade-off solutions, the joint graph convolutional model retains the highest accuracy while offering a handle to increase diversity. The proposed method can either trade accuracy to improve substantially the catalog coverage or the diversity within the list; or improve both by a lesser amount.