With an increasing focus on data privacy, there have been pilot studies on recommender systems in a federated learning (FL) framework, where multiple parties collaboratively train a model without sharing their data. Most of these studies assume that the conventional FL framework can fully protect user privacy. However, there are serious privacy risks in matrix factorization in federated recommender systems based on our study. This paper first provides a rigorous theoretical analysis of the server reconstruction attack in four scenarios in federated recommender systems, followed by comprehensive experiments. The empirical results demonstrate that the FL server could infer users' information with accuracy >80% based on the uploaded gradients from FL nodes. The robustness analysis suggests that our reconstruction attack analysis outperforms the random guess by >30% under Laplace noises with b no larger than 0.5 for all scenarios. Then, the paper proposes a new privacy-preserving framework based on homomorphic encryption, Privacy-Preserving Multi-View Matrix Factorization (PrivMVMF), to enhance user data privacy protection in federated recommender systems. The proposed PrivMVMF is successfully implemented and tested thoroughly with the MovieLens dataset.

Information has exploded on the Internet and mobile with the advent of the big data era. In particular, recommendation systems are widely used to help consumers who struggle to select the best products among such a large amount of information. However, recommendation systems are vulnerable to malicious user biases, such as fake reviews to promote or demote specific products, as well as attacks that steal personal information. Such biases and attacks compromise the fairness of the recommendation model and infringe the privacy of users and systems by distorting data.Recently, deep-learning collaborative filtering recommendation systems have shown to be more vulnerable to this bias. In this position paper, we examine the effects of bias that cause various ethical and social issues, and discuss the need for designing the robust recommendation system for fairness and stability.

Over the course of the last several years, a significant amount of scholarly attention has been drawn to the issue of feature selection. At a high level, feature selection can be considered as a branch of reducing data dimensionality of which the two primary methods are feature learning and feature selection. The problem of feature learning involves the creation of new features from the original data. In contrast, the feature selection problem does not change the original representation of the data variables, so the physical meaning of each variable is preserved. To be more specific, the feature selection problem can be subdivided into two scenarios: supervised and unsupervised. Since we do not have target variables, selecting unsupervised features is more challenging. Typically, the unsupervised feature selection relies on matrix decomposition (Cheng et al., 2005; Liberty et al., 2007; Martinsson et al., 2011; Lu, 2022a), filter (Dash et al., 2002), and embeddings (Dy & Brodley, 2004; Hou et al., 2011). On the other hand, matrix decomposition algorithms such as QR decomposition, and singular value decomposition have been used extensively over the years to reveal hidden structures of data matrices in scientific and engineering areas such as collaborative filtering (Marlin, 2003; Lim & Teh, 2007; Mnih & Salakhutdinov, 2007; Lu, 2022c;a), recommendation systems (Lu, 2022c), clustering and classification (Li et al., 2009; Wang et al., 2013).

Interested in recommender systems or do you just want to learn how to build advanced systems consisting of both frontend and backend? Then this course is all you need! You will learn how to setup a API using the programming language Python such that a backend recommender can be remotely called. Furthermore you will learn how to develop a fully working frontend system using Angular and firebase which is capable of presenting user recommendations. During this course you will use a vast range of technologies including Angular, Python, Typescript, MySQL and firebase.

You are talking on the phone, or recording an audio, or just speaking to voice assistants like Google Assistant, Cortana, or Alexa. But the person on the other side of the call cannot hear you because you are in a crowded place, the recorded audio has a lot of background noise, or the "Hey, Alexa" call wasn't picked up by your device because someone else started speaking. All of these problems related to separating voices, informally referred to as the "cocktail party problem", have been addressed using artificial intelligence and deep learning methods in recent years. But still, separating and inferring multiple simultaneous voices is a difficult problem to completely solve. To start, speech separation is extracting speech of the "wanted speaker" or "speaker of interest" from the overlapping mixture of speech from other speakers, also referred to as'noise'.

Due to the increasing trend of performing spamming activities (e.g., Web spam, deceptive reviews, fake followers, etc.) on various online platforms to gain undeserved benefits, spam detection has emerged as a hot research issue. Previous attempts to combat spam mainly employ features related to metadata, user behaviors, or relational ties. These works have made considerable progress in understanding and filtering spamming campaigns. However, this problem remains far from fully solved. Almost all the proposed features focus on a limited number of observed attributes or explainable phenomena, making it difficult for existing methods to achieve further improvement. To broaden the vision about solving the spam problem and address long-standing challenges (class imbalance and graph incompleteness) in the spam detection area, we propose a new attempt of utilizing signed latent factors to filter fraudulent activities. The spam-contaminated relational datasets of multiple online applications in this scenario are interpreted by the unified signed network. Two competitive and highly dissimilar algorithms of latent factors mining (LFM) models are designed based on multi-relational likelihoods estimation (LFM-MRLE) and signed pairwise ranking (LFM-SPR), respectively. We then explore how to apply the mined latent factors to spam detection tasks. Experiments on real-world datasets of different kinds of Web applications (social media and Web forum) indicate that LFM models outperform state-of-the-art baselines in detecting spamming activities. By specifically manipulating experimental data, the effectiveness of our methods in dealing with incomplete and imbalanced challenges is valida

Graphic footage: Fox News host Tucker Carlson weighs in on issues facing Americans ahead of the midterm elections on "Tucker Carlson Tonight." A Michigan man pleaded guilty last week to murdering, dismembering and eating the body parts of another man he met on a dating app. Mark David Latunski, 53, of Shiawassee County, Michigan, admitted in court last Thursday that he killed 25-year-old hairdresser Kevin Bacon after luring the University of Michigan-Flint student to his home in December 2019, according to local outlet Mlive.com. Latunski pleaded guilty as charged to mutilation of a body and to open murder, which encompasses murder in the first and second degree. Latunski acknowledged stabbing Bacon in the back and taking parts of his dead body to the kitchen, where he ate them, after meeting the young man on Grindr, which is a hookup app for gay, bisexual and transgender men.

Artificial Intelligence (AI) is one of the most discussed topics in the world of technology. It is also one of the most promising tools that people hope will improve our lives. For many, AI is something unbelievable and difficult to understand. However, we utilize AI in our day-to-day life in many ways without even knowing about it. According to statistics, only 33% of people know they use AI in their daily life.

By now, it will have been difficult to avoid the screenshots of the flirty messages Adam Levine sent to his fans - and the resulting memes. The Maroon 5 frontman is married to model Behati Prinsloo, with whom he has two children, and she is currently pregnant with their third. But, over the last week, five women have come forward accusing 43-year-old Levine of sending them explicit messages in recent years. The first was Instagram model Sumner Stroh, 23, who uploaded a bombshell TikTok video in which she claimed the two had had an affair last year and revealed a slew of flirtatious DMs. Then comedian Maryka and a woman named Alyson Rose shared screenshots of their alleged Instagram conversations with the singer. Levine's former yoga teacher, Alanna Zabel, next accused him of sending a dirty text to her, before fitness influencer Ashley Russell claimed he started messaging her on social media this year.

Machine learning (ML) is helping companies remain competitive. In fact, many companies' core business today is based on machine learning and image/speech recognition. Google, for example, uses machine learning in image recognition for Google Photos and speech recognition for Google Home and Google Assistant. Millions of people talk to Siri, Apple's virtual assistant. The company extended the application of its virtual assistant through HomePod, a smart home device.