To address this In the contemporary era of voluminous data [17], individuals are limitation, we propose Dimension Independent Mixup for Hard inundated with an incessant influx of content generated by the internet. Negative Sampling (DINS), which is the first Area-wise sampling To address the issue of information overload, Recommender method for training CF-based models. DINS comprises three modules: Systems (RecSys) are employed to assist users in locating the most Hard Boundary Definition, Dimension Independent Mixup, relevant information and are increasingly pivotal in online services and Multi-hop Pooling. Experiments with real-world datasets on such as news feed [30], music suggestion [5], and online shopping both matrix factorization and graph-based models demonstrate [9]. Collaborative filtering (CF) [13], a highly effective method that DINS outperforms other negative sampling methods, establishing that predicts a user's preference based on their past interactions, is its effectiveness and superiority. Our work contributes a new widely employed. The latest CF-based models [10, 28] incorporate perspective, introduces Area-wise sampling, and presents DINS historical interactions into condensed user/item vectors and predict as a novel approach that achieves state-of-the-art performance for a user's preference for each item based on the dot product of negative sampling.

High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neural networks, which form the backbone of many existing recommender systems, only leverage recognized user interests, falling short when it comes to efficiently uncovering unknown user preferences. While there has been some progress with neural contextual bandit algorithms towards enabling online exploration through neural networks, their onerous computational demands hinder widespread adoption in real-world recommender systems. In this work, we propose a scalable sample-efficient neural contextual bandit algorithm for recommender systems. To do this, we design an epistemic neural network architecture, Epistemic Neural Recommendation (ENR), that enables Thompson sampling at a large scale. In two distinct large-scale experiments with real-world tasks, ENR significantly boosts click-through rates and user ratings by at least 9% and 6% respectively compared to state-of-the-art neural contextual bandit algorithms. Furthermore, it achieves equivalent performance with at least 29% fewer user interactions compared to the best-performing baseline algorithm. Remarkably, while accomplishing these improvements, ENR demands orders of magnitude fewer computational resources than neural contextual bandit baseline algorithms.

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers on LLMs for recommendation, https://github.com/WLiK/LLM4Rec.

AI might slowly be taking over the world but it is still lagging behind at online dating, a study claims. Singletons are far more likely to swipe right on a real-life profile compared to one written by ChatGPT. The results found 36 per cent of women said'Yes' on Tinder to an AI-generated male but this rose to almost 64 per cent who did so for genuine life details. But it was far closer for men, who swiped right for 46 per cent of AI summaries and 54 per cent of those written by humans. Alex Limanowka, a relationship coach and psychotherapist, said: 'This gender disparity suggests that men often rely more on photos and may swipe right without reading the woman's profile.'

The background-checking tool used by Match Group to offer a safety feature for Tinder users is shutting down. The non-profit and female-founded Garbo, which the dating app conglomerate has partnered with since 2019, will shut down its consumer tool at the end of August. "Most tech companies just see trust and safety as good PR," Kathryn Kosmides, Garbo's founder and CEO, told The Wall Street Journal, which published a report on the severed partnership. "I'd rather Garbo shift focus to our other efforts than allow the vision of Garbo to be compromised and relegated to a piece of big corporations' marketing goals." A Match Group spokesperson supplied a statement to Engadget.

Kurt "The CyberGuy" Knutsson provides tips on how to limit the device's notifications. Do you ever feel like your Alexa device is listening to you a little too much? Do you wish you could have more control over what it says and when it says it? It's great that they can be set up to lock our doors, turn off our lights or give us the weather. STAY UPDATED WITH KURT'S FREE CYBERGUY NEWSLETTER TO GET SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER What I don't appreciate about Alexa devices, however, is that they sometimes can give us extra information that we never asked for, which can be annoying.

Amazon's virtual assistant Alexa has been accused of sexism after being unable to respond to a question about the Lionesses' World Cup semi-final. British academic Dr Joanne Rodda asked Alexa for the result of Wednesday's match against Australia, which England won 3-1. But the supposedly'smart' technology didn't even know the match had taken place as it was only familiar with the men's game, the BBC reports. Astonishingly, when Dr Rodda asked'for the result of the England-Australia football match', Alexa said there was no such match. Amazon admitted the mistake was due to an'error' – although it didn't specify the cause – and that Alexa will get better at learning over time.

Microsoft has been filling up Windows builds with questionably necessary add-on programs for decades, and for just as long, power users have been stripping them out. Some have gone so far as to create custom installation media with trimmed-down builds. But in an upcoming Windows 11 release, you won't need to go to such drastic lengths, at least for some of the most common occupants of the cutting room floor. A new Windows Insider build lets users remove more default apps without any third-party tools. In particular, Canary build 25931 lets users uninstall the default Windows programs Camera, Photos, People, and the Remote Desktop Client via the default uninstall interface in Settings, as well as the recently decommissioned Cortana virtual assistant.

Dr Rodda, a senior lecturer in psychiatry at Kent and Medway Medical School - with an interest in artificial intelligence (AI), said she had only been able to get an answer from Alexa when she specified it was women's football she was interested in,.

Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through historical user-item interactions, which may potentially violate user privacy. Additionally, these approaches often overlook the significance of the temporal fluctuation in item popularity that can sway users' decision-making. To bridge this gap, we propose Popularity-Aware Recommender (PARE), which makes non-personalized recommendations by predicting the items that will attain the highest popularity. PARE consists of four modules, each focusing on a different aspect: popularity history, temporal impact, periodic impact, and side information. Finally, an attention layer is leveraged to fuse the outputs of four modules. To our knowledge, this is the first work to explicitly model item popularity in recommendation systems. Extensive experiments show that PARE performs on par or even better than sophisticated state-of-the-art recommendation methods. Since PARE prioritizes item popularity over personalized user preferences, it can enhance existing recommendation methods as a complementary component. Our experiments demonstrate that integrating PARE with existing recommendation methods significantly surpasses the performance of standalone models, highlighting PARE's potential as a complement to existing recommendation methods. Furthermore, the simplicity of PARE makes it immensely practical for industrial applications and a valuable baseline for future research.