Boostr, the only pipeline-to-profits advertising management platform for the media industry announced the launch of Proposal-IQ, a groundbreaking new product designed to help media sales organizations improve the quality of RFP responses while simultaneously building proposals faster. By suggesting the optimal media mix and media plan for each client's objectives and tying into the media seller's real-time inventory, Proposal-IQ drives larger deal sizes, increases in average products sold per order, and higher sell-through--helping media sales organizations grow their revenue. Media companies typically send out dozens of proposals each week and have less than 48 hours to respond to RFPs, creating an enormous burden on media sales teams who need to obtain pricing approvals, conduct inventory checks and complete multiple reviews as part of a highly manual process. Time constraints and repetitiveness often lead salespeople and planners to pitch the same products repeatedly, and their choices are often based more on familiarity than on performance data. Unfortunately, only 37% of proposals contain multiple products yet the top quartile of highest growing publishers are selling multiple products on 48% of proposals.

From reminding potty-training toddlers to go to the loo to telling bedtime stories and being used as a "conversation partner", voice-activated smart devices are being used to help rear children almost from the day they are born. But the rapid rise in voice assistants, including Google Home, Amazon Alexa and Apple's Siri could, researchers suggest, have a long-term impact on children's social and cognitive development, specifically their empathy, compassion and critical thinking skills. "The multiple impacts on children include inappropriate responses, impeding social development and hindering learning opportunities," said Anmol Arora, co-author of an article published in the journal Archives of Disease in Childhood. A key concern is that children attribute human characteristics and behaviour to devices that are, said Arora, "essentially a list of trained words and sounds mashed together to make a sentence." The children anthropomorphise and then emulate the devices, copying their failure to alter their tone, volume, emphasis or intonation.

From reminding potty-training toddlers to go to the loo to telling bedtime stories and being used as a "conversation partner", voice-activated smart devices are being used to help rear children almost from the day they are born. But the rapid rise in voice assistants, including Google Home, Amazon Alexa and Apple's Siri could, new research suggests, have a long-term impact on children's social and cognitive development, specifically their empathy, compassion and critical thinking skills. "The multiple impacts on children include inappropriate responses, impeding social development and hindering learning opportunities," said Anmol Arora, co-author of research published in the journal Archives of Disease in Childhood. A key concern is that children attribute human characteristics and behaviour to devices that are, said Arora, "essentially a list of trained words and sounds mashed together to make a sentence." The children anthropomorphise and then emulate the devices, copying their failure to alter their tone, volume, emphasis or intonation.

We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement learning from human feedback to train our models with two new additions to help human raters judge agent behaviour. First, to make our agent more helpful and harmless, we break down the requirements for good dialogue into natural language rules the agent should follow, and ask raters about each rule separately. We demonstrate that this breakdown enables us to collect more targeted human judgements of agent behaviour and allows for more efficient rule-conditional reward models. Second, our agent provides evidence from sources supporting factual claims when collecting preference judgements over model statements. For factual questions, evidence provided by Sparrow supports the sampled response 78% of the time. Sparrow is preferred more often than baselines while being more resilient to adversarial probing by humans, violating our rules only 8% of the time when probed. Finally, we conduct extensive analyses showing that though our model learns to follow our rules it can exhibit distributional biases.

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