Recommender Systems are giving increasing importance to the beyond accuracy quality measures, and reliability is one of the most important in the Collaborative Filtering context. This paper proposes Bernoulli Matrix Factorization (BeMF), a matrix factorization model to provide both prediction values and reliability ones. This is a very innovative approach from several perspectives: a) it acts on the model-based Collaborative Filtering, rather than in the memory-based one, b) it does not use external methods or extended architectures for providing reliability such as the existing solutions, c) it is based on a classification-based model, instead of the usual regression-based ones, and d) the matrix factorization formalism is supported by the Bernoulli distribution, to exploit the binary nature of the designed classification model. As expected, results show that the more reliable a prediction is, the less liable to be wrong: recommendation quality has been improved by selecting the most reliable predictions. State-of-the-Art quality measures for reliability have been tested, showing improved results compared to the baseline methods and models.

Innovation and its advancements have gotten fundamental to our way of life in the digital age. In a digitally dominated world, a huge amount of information gets produced each second at an alarming rate. The flood of Big Data and Data Science has made it important to utilize raw information to make a few data-driven decisions. The question remains, how on the planet are we going to deal with this data. The appropriate response is through Natural Language Processing (NLP).

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A convicted paedophile who was snared by a vigilante group is to have his case examined at the UK Supreme Court. Judges at the UK's highest court will consider whether prosecutions based on the covert operations of "paedophile hunters" breach the right to privacy. Mark Sutherland, 37, believed he was communicating with a 13-year-old boy on the dating app Grindr. But in reality it was a 48-year-old man who was part of a group called Groom Resisters Scotland. The Supreme Court will hold a virtual hearing to consider the case and will issue its judgement later.

Grindr is removing an "ethnicity filter" from its dating app as part of its support for the Black Lives Matter movement, the company announced on Monday. The controversial feature, limited to those who stump up £12.99 a month for the premium version of the app, allows users to sort search results based on reported ethnicity, height, weight and other characteristics. In a statement posted to Instagram, the company said "We stand in solidarity with the #BlackLivesMatter movement and the hundreds of thousands of queer people of color who log in to our app every day. "We will continue to fight racism on Grindr, both through dialogue with our community and a zero-tolerance policy for racism and hate speech on our platform. As part of this commitment, and based on your feedback, we have decided to remove the ethnicity filter from our next release.

Dating app Grindr has said it will remove its'ethnicity filter' that allows users to search potential matches by race. Singletons prepared to pay £12.99-a-month for the'premium' service are currently able to sort users based on their ethnicity, weight, height, and other characteristics. But less than 24 hours after its tweet supporting'Black Lives Matter' received widespread condemnation over the filter, the company has said it will delete it. Protests have rocked the US for six days following the death of George Floyd, who was filmed gasping'I can't breathe' as an officer knelt on his neck in Logan County, West Virginia. Writing on Twitter, the app said: 'As part of our commitment to (Black Lives Matter), we have decided to remove the ethnicity filter from our next release.

Dating app Grindr will finally remove its ethnicity filter, following years of criticism culminating in accusations of hypocrisy regarding the company's stance on #BlackLivesMatter. The app currently lets users filter potential matches based on age, height, weight and ethnicity, but the company -- which says it has a "zero-tolerance policy for racism and hate speech" -- has confirmed the ethnicity filter will be removed from the next version of the app. The change, which coincides with the start of Pride month, appears to have been catalyzed by responses to a tweet in which Grindr said, "Demand justice. One response to the tweet said "remove the ethnicity filter" and was subsequently retweeted 1,000 times. Grindr later deleted its original tweet, replacing it with the below.

Next generation virtual assistants are envisioned to handle multimodal inputs (e.g., vision, memories of previous interactions, etc., in addition to the user's utterances), and perform multimodal actions (e.g., displaying a route in addition to generating the system's utterance). We introduce Situated Interactive MultiModal Conversations (SIMMC) as a new direction aimed at training agents that take multimodal actions grounded in a co-evolving multimodal input context in addition to the dialog history. We provide two SIMMC datasets totalling ~13K human-human dialogs (~169K utterances) using a multimodal Wizard-of-Oz (WoZ) setup, on two shopping domains: (a) furniture (grounded in a shared virtual environment) and, (b) fashion (grounded in an evolving set of images). We also provide logs of the items appearing in each scene, and contextual NLU and coreference annotations, using a novel and unified framework of SIMMC conversational acts for both user and assistant utterances. Finally, we present several tasks within SIMMC as objective evaluation protocols, such as Structural API Prediction and Response Generation. We benchmark a collection of existing models on these SIMMC tasks as strong baselines, and demonstrate rich multimodal conversational interactions. Our data, annotations, code, and models will be made publicly available.

As voice-based assistants such as Alexa, Siri, and Google Assistant become ubiquitous, users increasingly expect to maintain natural and informative conversations with such systems. However, for an open-domain conversational system to be coherent and engaging, it must be able to maintain the user's interest for extended periods, without sounding boring or annoying. In this paper, we investigate one natural approach to this problem, of modulating response prosody, i.e., changing the pitch and cadence of the response to indicate delight, sadness or other common emotions, as well as using pre-recorded interjections. Intuitively, this approach should improve the naturalness of the conversation, but attempts to quantify the effects of prosodic modulation on user satisfaction and engagement remain challenging. To accomplish this, we report results obtained from a large-scale empirical study that measures the effects of prosodic modulation on user behavior and engagement across multiple conversation domains, both immediately after each turn, and at the overall conversation level. Our results indicate that the prosody modulation significantly increases both immediate and overall user satisfaction. However, since the effects vary across different domains, we verify that prosody modulations do not substitute for coherent, informative content of the responses. Together, our results provide useful tools and insights for improving the naturalness of responses in conversational systems.

Even if your grad has finally made it through college, that doesn't mean they're ready to step out into the real world with no help. They'll not only have to find a job but also might need a little help living on their own, taking on more responsibility and being more of an adult in general. That includes having better security practices, dressing smarter and, if they're lucky enough to find their own apartment, making their new place feel more like home. Here are a few gadgets that could help ease the transition into "adulthood." It's not a terribly sexy subject, but keeping your online data safe should be a priority for everyone, including your new grad.