Recommender systems play a pivotal role in modern content platforms, yet traditional behavior-based models often face challenges in addressing cold users with sparse interaction data. Engaging these users, however, remains critical for sustaining platform growth. To tackle this issue, we propose leveraging reconstructed social graph to complement interest representations derived from behavioral data. Despite the widespread availability of social graphs on content platforms, their utility is hindered by social-relation noise and inconsistencies between social and behavioral interests. To mitigate noise propagation in graph data and extract reliable social interests, we introduce a dual-view denoising framework. This approach first applies low-rank singular value decomposition (SVD) to the user-item interaction matrix, generating denoised user embeddings for reconstructing the social graph. It then employs contrastive learning to align the original and reconstructed social graphs. To address the discrepancy between social and behavioral interests, we utilize a mutual distillation mechanism that decomposes interests into four subcategories: aligned social/behavioral interests and social/behavioral-specific interests, enabling effective integration of the two. Empirical results demonstrate the efficacy of our method, particularly in improving recommendations for cold users, by combining social and behavioral data. The implementation of our approach is publicly available at https://github.com/WANGLin0126/CLSRec.

On Monday, we saw Messari publish their report on decentralised machine learning, seeing through the hype and addressing the crucial need to prevent big tech from monopolising AI in the future. I chose to focus mostly on Bittensor in the extracts below. "The network enables individuals to contribute to foundational models and monetize their work, regardless of the size or niche of their contributions. This is akin to how the internet made niche contributions economically viable and empowered individuals on content platforms like YouTube. " […] "The teams behind the projects discussed in this report are not simply building a crypto-based network for hype, but they are teams of AI researchers and engineers who have realized the potential of crypto to address issues in their industry."

It has been experimentally shown that communities in social networks tend to have a core-periphery topology. However, there is still a limited understanding of the precise structure of core-periphery communities in social networks including the connectivity structure and interaction rates between agents. In this paper, we use a game-theoretic approach to derive a more precise characterization of the structure of core-periphery communities.

Deployed on quality content platforms, the world's first knowledge dashboard leverages innovative technology to show users how much they know about the topics they care about CRUX, the developer of Knowledge Quantification technology used, is launching Knowledge Hub for deployment on quality content platforms and publisher sites. Knowledge Hub provides each user with a complete view of all the topics they are reading about – and how much of each topic they have already covered. The Knowledge Hub shows users in real time the impact of important content they have not yet read – and provides a personalized knowledge journey of the best articles to increase their knowledge. Knowledge Hub is the latest user experience based on CRUX's innovative knowledge quantification technology that measures each user's knowledge based on the content they consume. Deployed on quality publishers like NIKKEI, Sifted and The American Prospect, the technology is revolutionizing user engagement, retention and conversion.

The Internet is ripe with toxic content. Social Media companies such as Facebook, Twitter, Instagram, etc., have been using a combination of human content moderators and technology to try to limit the amount of harmful content. With the advancement of AI algorithms, companies such as Spectrum Labs are rushing into the space to use contextual AI algorithms to improve toxic content detection. They've just raised a 10 million round of funding in September 2020. It seems content platforms want more sophisticated solutions.

A survey by U.S. consultancy Boston Consulting Group shows that 70% of young people are motivated to shop by browsing or viewing media content. As a result, content-based ads are becoming a new trend in the e-commerce marketplace. Live commerce platforms and image- and video-sharing social media are also winning the hearts of fickle consumers by stimulating consumer appetite via the content. While there is a lot of content that can be monetized on China's Twitter-like microblogging site Weibo and video-sharing platforms, existing methods like spot ads do not appeal to viewers or hamper users' viewing experience. Markable AI (mai), launched in 2016, is an artificial intelligence-based solution for content recognition technology aimed at optimizing content ads.

The artificial intelligence applied research startup Abzu identifies false news with its proprietary QLattice. In the latest Reuters Institute Digital News Report, less than four in ten people said that they trust most news most of the time (that's 38% surveyed in January 2020, a fall of four percentage points from 2019)¹. Today's global crises make it all too obvious the necessity for dependable and factual journalism, yet we are exposed to a continuum of information authored by innumerable sources with debatable credentials. Slaves to our most basic emotions -- fear, disgust, and surprise² -- we are inflamed by an addictive negative feedback loop of our own creation. We crave the truth, but data shows we force-feed ourselves lies.

Could it be some crazy coincidence? I mean after all, we are shaped by the things we see, read and experience. After all, crazier things have happened. But it was not a coincidence. What made the plagiarism abundantly clear was the perpetrator's lack of command of the English language. In trying to put my article into his own words, he made a couple of telling errors. Most noticeably, he tried to reuse a phrase that I had coined in my article, but had misinterpreted its intent and meaning entirely.

UK startup Fabula AI reckons it's devised a way for artificial intelligence to help user generated content platforms get on top of the disinformation crisis that keeps rocking the world of social media with antisocial scandals. Even Facebook's Mark Zuckerberg has sounded a cautious note about AI technology's capability to meet the complex, contextual, messy and inherently human challenge of correctly understanding every missive a social media user might send, well-intentioned or its nasty flip-side. "It will take many years to fully develop these systems," the Facebook founder wrote two years ago, in an open letter discussing the scale of the challenge of moderating content on platforms thick with billions of users. "This is technically difficult as it requires building AI that can read and understand news." But what if AI doesn't need to read and understand news in order to detect whether it's true or false? Step forward Fabula, which has patented what it dubs a "new class" of machine learning algorithms to detect "fake news" -- in the emergent field of "Geometric Deep Learning"; where the datasets to be studied are so large and complex that traditional machine learning techniques struggle to find purchase on this'non-Euclidean' space. The startup says its deep learning algorithms are, by contrast, capable of learning patterns on complex, distributed data sets like social networks.

To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study. In Phase 1, contextual logs (URLs) are utilized to assess active usage of the content platform. If there is active use, the appearance information is utilized in Phase 2 to infer behavioral engagement. Incorporating the contextual information improved the overall F1-scores from 0.77 to 0.82. Our cross-classroom and cross-platform experiments showed the proposed generic and multi-modal behavioral engagement models' applicability to a different set of students or different subject areas.