Personal Assistant Systems
Trump, Iran and the Denial of Defeat
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How Will the New Prime Minister Be Chosen--and How Soon Could They Be in Downing Street?
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Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Recommenders aim to rank items from a discrete item corpus in line with user interests, yet suffer from extremely sparse user preference data. Recent advances in diffusion models have inspired diffusion-based recommenders, which alleviate sparsity by injecting noise during a forward process to prevent the collapse of perturbed preference distributions. However, current diffusion-based recommenders predominantly rely on continuous Gaussian noise, which is intrinsically mismatched with the discrete nature of user preference data in recommendation. In this paper, building upon recent advances in discrete diffusion, we propose PreferGrow, a discrete diffusion-based recommender system that models preference ratios by fading and growing user preferences over the discrete item corpus. PreferGrow differs from existing diffusion-based recommenders in three core aspects: (1) Discrete modeling of preference ratios: PreferGrow models relative preference ratios between item pairs, rather than operating in the item representation or raw score simplex.
Young People Are Sounding the Alarm on AI. We Should Listen to Them
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Why You Trust Your Nurse More Than Your Doctor
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Breaking Down the Time-Slip Ending of My Royal Nemesis
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Trump Admin Blames and Arrests Alleged Vandals for Reflecting Pool Problems
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Read British Prime Minister Keir Starmer's Resignation Speech in Full
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Trump and Italy's Giorgia Meloni Feud Over Photo
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Colombia Elects Trump-Backed Populist in Presidential Election
The Latin American nation is poised to swing right as nationalist lawyer and political novice Abelardo de la Espriella will succeed the country’s first leftist President, Gustavo Petro.