Bernie Moreno, a Republican U.S. Senate candidate from Ohio, discusses the GOP's eagerness to retake the Senate in November, the illegal immigration crisis and Nikki Haley's refusal to drop out of the primary race. Republican Ohio Senate candidate Bernie Moreno is blasting the Associated Press after a story published days before the primary election linking him to an adult online dating site, which a former intern has taken credit for creating, was called into question by the dating site's founder. On Friday, a post on X from one of the founders of the online site Adult Friend Finder, who says he wrote "most of the early code," seemingly rejected a key aspect of an Associated Press report days earlier that suggested "geolocation data," which is commonly understood as involving an IP address or GPS, linked the account to the area of a Moreno family home. "I reviewed all the available information and it showed that the account had only a single visit, no activity, no profile photo, consistent with a prank or someone just checking out the site," Andrew Conru, the engineer who founded Adult Friend Finder, wrote on social media. "The AP report seeming to claim that the available data proves the account was created in Florida is inaccurate, as location information is manually entered during the signup (sic) process. In reality, there appears to be no public geolocation data tied to the account."

Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high efficiency in big data processing. However, with the dramatically increased number of users/items in current RSs, the computational complexity for training a MF model largely increases. Many existing works have accelerated MF, by either putting in additional computational resources or utilizing parallel systems, introducing a large cost. In this paper, we propose algorithmic methods to accelerate MF, without inducing any additional computational resources. In specific, we observe fine-grained structured sparsity in the decomposed feature matrices when considering a certain threshold. The fine-grained structured sparsity causes a large amount of unnecessary operations during both matrix multiplication and latent factor update, increasing the computational time of the MF training process. Based on the observation, we firstly propose to rearrange the feature matrices based on joint sparsity, which potentially makes a latent vector with a smaller index more dense than that with a larger index. The feature matrix rearrangement is given to limit the error caused by the later performed pruning process. We then propose to prune the insignificant latent factors by an early stopping process during both matrix multiplication and latent factor update. The pruning process is dynamically performed according to the sparsity of the latent factors for different users/items, to accelerate the process. The experiments show that our method can achieve 1.2-1.65 speedups, with up to 20.08% error increase, compared with the conventional MF training process. We also prove the proposed methods are applicable considering different hyperparameters including optimizer, optimization strategy and initialization method.

Dyslexia is the most widespread specific learning disorder and significantly impair different cognitive domains. This, in turn, negatively affects dyslexic students during their learning path. Therefore, specific support must be given to these students. In addition, such a support must be highly personalized, since the problems generated by the disorder can be very different from one to another. In this work, we explored the possibility of using AI to suggest the most suitable supporting tools for dyslexic students, so as to provide a targeted help that can be of real utility. To do this, we relied on recommendation algorithms, which are a branch of machine learning, that aim to detect personal preferences and provide the most suitable suggestions. We hence implemented and trained three collaborative-filtering recommendation models, namely an item-based, a user-based and a weighted-hybrid model, and studied their performance on a large database of 1237 students' information, collected with a self-evaluating questionnaire regarding all the most used supporting strategies and digital tools. Each recommendation model was tested with three different similarity metrics, namely Pearson correlation, Euclidean distance and Cosine similarity. The obtained results showed that a recommendation system is highly effective in suggesting the optimal help tools/strategies for everyone. This demonstrates that the proposed approach is successful and can be used as a new and effective methodology to support students with dyslexia.

Police in Washington state announced two suspects were arrested in connection with the murder of a missing elderly man who was allegedly poisoned with fentanyl by a pair who gained his trust through a dating app. The Mercer Island Police released a statement saying Philip J. Brewer, 32, and Christina Hardy, 47, are facing charges for the murder of Curtis Engeland, 74, by using an elaborate scheme to defraud and murder him. Police said that Brewer and Hardy are believed to have become acquainted with Engeland several months ago and subsequently financially defrauded him. Police also believe the suspects later violently confronted Engeland at his Mercer Island home in the late evening hours of February 23, and used Engeland's vehicle to leave Mercer Island that night. POLICE MADE'A DEAL WITH THE DEVIL' TO UNCOVER LOCATION OF MISSING BLOOD MOUNTAIN HIKER: KILLER WAS'HUNTING' Two suspects were arrested in connection to the homicide of missing Mercer Island resident Curtis Engeland, 74.

Amazon's Echo Buds are on sale for their lowest price yet. You can get the retailer's AirPods alternatives today for a mere 35 (that's 15 off). One of Engadget's picks for the best budget wireless earbuds, the Echo Buds have a clear, detailed and balanced sound profile, built-in Alexa support and five hours of battery life. The discount comes a few days before Amazon's Big Spring Sale is set to kick off on March 20. Amazon's Echo Buds are available today for a record-low price.

This poses challenges for applications interested in targeting specific demographics (e.g., an African American business or NGO; a voice-tutoring system for children that are not of White ethnicity, etc.). The ultimate goal of the project described in this paper is to provide to designers, developers, and enterprises the choice of having a professional voice which is clearly recognizable as African American, and therefore more able to address diversity and inclusiveness issues. Being more precise, our goal is to create an African American Text-to-Speech system, which we will refer simply as an African American voice or AA voice, able to produce synthetic audio segments from standard English texts, and which will be recognized by African American speakers and non-speakers as sounding like a native African American speaker. The AA voice should exhibit a level of technical quality similar to the Standard American English (SAE) synthetic voices currently available through professional platforms. The evaluation of the technical quality of the AA voice, however, is not addressed in this paper, which focuses primarily on whether the AA voice can be recognized as sounding like an African American speaker. Linguists [27, 28] have described a continuum of dialects under what is often termed African American Vernacular English (AAVE). At one end of the spectrum, one finds the largest deviation from SAE in terms of lexicon (including slang), syntax and morphology, and phonological/phonetic properties. At the other end, AAVE speakers begin to approach SAE in terms of lexicon and grammar but still retain marked speech characteristics (primarily in terms of intonation, phonation, and vowel placement [14, 28]) which grant the speech a distinctive identity which listeners use as cues in the perception of African American English [44].

Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users' current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users' personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptly captured sequential patterns and transitional relationships within users' check-in trajectories, a noticeable gap persists in devising a mechanism for discerning specialized behavioral patterns during distinct time slots, such as noon, afternoon, or evening. In this paper, we introduce an innovative data structure termed the ``Mobility Tree'', tailored for hierarchically describing users' check-in records. The Mobility Tree encompasses multi-granularity time slot nodes to learn user preferences across varying temporal periods. Meanwhile, we propose the Mobility Tree Network (MTNet), a multitask framework for personalized preference learning based on Mobility Trees. We develop a four-step node interaction operation to propagate feature information from the leaf nodes to the root node. Additionally, we adopt a multitask training strategy to push the model towards learning a robust representation. The comprehensive experimental results demonstrate the superiority of MTNet over ten state-of-the-art next POI recommendation models across three real-world LBSN datasets, substantiating the efficacy of time slot preference learning facilitated by Mobility Tree.

This work introduces Divide-Factor-Combine (DFC), a parallel divide-andconquer framework for noisy matrix factorization.

Recommendation systems can provide accurate recommendations by analyzing user shopping history. A richer user history results in more accurate recommendations. However, in real applications, users prefer e-commerce platforms where the item they seek is at the lowest price. In other words, most users shop from multiple e-commerce platforms simultaneously; different parts of the user's shopping history are shared between different e-commerce platforms. Consequently, we assume in this study that any e-commerce platform has a complete record of the user's history but can only access some parts of it. If a recommendation system is able to predict the missing parts first and enrich the user's shopping history properly, it will be possible to recommend the next item more accurately. Our recommendation system leverages user shopping history to improve prediction accuracy. The proposed approach shows significant improvements in both NDCG@10 and HR@10.

Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that have prevailed for decades. On one hand, IDRec often faces significant performance degradation on cold-start problem; on the other hand, IDRec cannot use longer training data due to constraints imposed by iteration efficiency. Most prior studies alleviate the above problems by introducing pre-trained knowledge(e.g. pre-trained user model or multi-modal embeddings). However, the explosive growth of online latency can be attributed to the huge parameters in the pre-trained model. Therefore, most of them cannot employ the unified model of end-to-end training with IDRec in industrial recommender systems, thus limiting the potential of the pre-trained model. To this end, we propose a $\textbf{P}$re-trained $\textbf{P}$lug-in CTR $\textbf{M}$odel, namely PPM. PPM employs multi-modal features as input and utilizes large-scale data for pre-training. Then, PPM is plugged in IDRec model to enhance unified model's performance and iteration efficiency. Upon incorporating IDRec model, certain intermediate results within the network are cached, with only a subset of the parameters participating in training and serving. Hence, our approach can successfully deploy an end-to-end model without causing huge latency increases. Comprehensive offline experiments and online A/B testing at JD E-commerce demonstrate the efficiency and effectiveness of PPM.