782 research outputs found

    A yes vote in the Scottish referendum would start a serious debate about independence for Wales

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    Roger Sculley argues that while independence for Wales does not presently enjoy significant backing, this would change with a Yes vote in the Scottish independence referendum. It would offer a clear example to which advocates of independence could point and would alter the fundamental character of the UK rump that remained

    Initial experimental evidence that the ability to choose between items alters attraction to familiar versus novel persons in different ways for men and women

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    Nonhuman species may respond to novel mates with increased sexual motivation (‘The Coolidge Effect1). In humans, novel technological advances, such as online dating platforms, are thought to result in ‘Choice Overload’2. This may undermine the goal of finding a meaningful relationship3, orienting the user toward novel possible partners versus committing to a partner. Here, we used a paradigm measuring change in attraction to familiar faces (i.e. rated on second viewing4) to investigate Coolidge-like phenomena in humans primed with choice of potential online dating partners. We examined two pre-registered hypotheses (https://osf.io/xs74r/files/). First, whether experimentally priming choice (viewing a slideshow of online dating images) directly reduces the attractiveness of familiar preferred sex faces compared to our control condition. Second, whether the predicted effect is stronger for men than women given the role of the Coolidge effect in male sexual motivation5.<br/

    Fast Matrix Factorization for Online Recommendation with Implicit Feedback

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    This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure
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