Mashable Voices Mashable Selects Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Switch Off Trending Now In My Bag All Series You may have just gotten Alexa+ AI for free, even if you don't have Amazon Prime Amazon is rolling out its AI assistant to Fire TV users at not cost. Amazon's Alexa+ AI assistant is now free for Fire TV users. Have an Amazon Fire TV device? Well, you just got Alexa+, Amazon's AI assistant, for free -- whether you like it or not. On Wednesday, Amazon announced that the company would now include Alexa+ at no cost with every Fire TV device.

Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Coming home to clean floors doesn't have to cost more than a few hundred dollars. Leah Stodart is a Philadelphia-based Senior Shopping Reporter at Mashable where she covers and tests essential home tech like vacuums, TVs, beauty devices, and eco-friendly hacks. Her ever-evolving experience in these categories helps her make thoughtful recommendations for how to spend your money during shopping holidays like Black Friday, which Leah has been covering for Mashable since 2017. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. Budget-friendly robot vacuums are way smarter than they used to be.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Creator Hub Gift Ideas For Everyone On Your List Mashable Selects Versus Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series Spotify will now tell you when an artist isn't real The new AI Persona badge will flag synthetic artist identities and keep their music out of recommendations by default. Olivia Tauber is the deputy editor of digital culture, covering creators, media, movies, beauty, and more. Based in New York, her work has appeared in The New York Times, Vanity Fair, The Cut, Teen Vogue, Complex, and Interview Magazine. She holds a Master's degree in Journalism from NYU and a Bachelor's from the University of Michigan. She also runs Fan Mail, a weekly pop-culture newsletter.

This past March, Google introduced a chatbot inside of Maps as part of its overhaul of the software's navigation suite. The company pitched the feature, Ask Maps, as a way to give users a way to obtain information no traditional map can provide. Today, Google is expanding what Ask Maps can do, starting with the addition of new agentic capabilities. The next time you feel like ordering food, you can do so directly through Ask Maps. In a press briefing, Amanda Leicht-Moore, senior product director of Google Maps, demoed the feature by telling the chatbot she wanted recommendations for avocado toast and an oat milk latte near her home.

PCWorld highlights how cybersecurity experts often provide conflicting advice due to different risk assessments and varying contexts behind recommendations. Recent developments include Xfinity's $117.5 million data breach settlement with a September 14 filing deadline and Microsoft's AI-enhanced Windows security updates. Understanding nuanced context is crucial since simplified advice like "don't use public Wi-Fi" typically means avoiding sensitive tasks rather than complete avoidance. Cybersecurity advice is sometimes extremely straightforward.

Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information, but remains challenged by modality-specific noise, semantic inconsistency, and unstable propagation over user-item graphs. These issues are often exacerbated by naive fusion or shallow modeling strategies, leading to degraded generalization and poor robustness. While recent work has explored the frequency domain as a lens to separate stable from noisy signals, most methods rely on static filtering or reweighting, lacking the ability to reason over spectral structure or adapt to modality-specific reliability. To address these challenges, we propose a Structured Spectral Reasoning (SSR) framework for frequency-aware multimodal recommendation. Our method follows a four-stage pipeline: (i) Decompose graph-based multimodal signals into spectral bands via graph-guided transformations to isolate semantic granularity; (ii) Modulate band-level reliability with spectral band masking, a training-time masking with representation-consistency objective that suppresses brittle frequency components; (iii) Fuse complementary frequency cues using hyperspectral reasoning with low-rank cross-band interaction; and (iv) Align modality-specific spectral features via contrastive regularization to promote semantic and structural consistency. Experiments on three real-world benchmarks show consistent gains over strong baselines, particularly under sparse and cold-start settings. Additional analyses indicate that structured spectral modeling improves robustness and provides clearer diagnostics of how different bands contribute to performance. The code is available at https://github.com/llm-ml/SSR.git.

Traditional recommender systems have relied heavily on positive feedback for learning user preferences, while the abundance of negative feedback in real-world scenarios remains underutilized. To address this limitation, recent years have witnessed increasing attention on leveraging negative feedback in recommender systems to enhance recommendation performance. However, existing methods face three major challenges: limited model compatibility, ineffective information exchange, and computational inefficiency. To overcome these challenges, we propose a modelagnostic Signed Dual-Channel Graph Contrastive Learning (SDCGCL) framework that can be seamlessly integrated with existing graph contrastive learning methods. The framework features three key components: (1) a Dual-Channel Graph Embedding that separately processes positive and negative graphs, (2) a Cross-Channel Distribution Calibration mechanism to maintain structural consistency, and (3) an Adaptive Prediction Strategy that effectively combines signals from both channels. Building upon this framework, we further propose a Dual-channel Feedback Fusion (DualFuse) model and develop a two-stage optimization strategy to ensure efficient training. Extensive experiments on four public datasets demonstrate that our approach consistently outperforms state-of-the-art baselines by substantial margins while exhibiting minimal computational complexity.

Meta's Oversight Board has called on the social media company to strengthen its protection for ordinary people targeted by sexualized deepfakes. The Board recommends the addition of AI-generated impersonations in Meta's Adult Sexual Exploitation policy, arguing that those images and videos are non-consensual by default. It also wants Meta to allow users to designate connected accounts, such as trusted friends and family, who can report potential violations like non-consensual intimate imagery on their behalf. Finally, the Board recommends making AI-generated sexual impersonation a separate category from harassment and nudity in the company's content reporting and appeal forms. At the moment, only the residents of Texas and Florida have access to a specialized form that lists deepfake intimate imagery as a reason for the report.

Large language models (LLMs) have shown that generative pretraining can distill vast world knowledge into compact token representations. While LLMs encapsulate extensive world knowledge, they remain limited in modeling the behavioral knowledge contained within user interaction histories. User behavior forms a distinct modality, where each action--defined by multi-dimensional attributes such as time, context, and transaction type--constitutes a behavioral token. Modeling these high-cardinality, sparse, and irregular sequences is challenging, and discriminative models often falter under limited supervision. To bridge this gap, we extend generative pretraining to user behavior, learning transferable representations from unlabeled behavioral data analogous to how LLMs learn from text.

Contrastive learning has shown effectiveness in improving sequential recommendation models. However, existing methods still face challenges in generating high-quality contrastive pairs: they either rely on random perturbations that corrupt user preference patterns or depend on sparse collaborative data that generates unreliable contrastive pairs. Furthermore, existing approaches typically require predefined selection rules that impose strong assumptions, limiting the model's ability to autonomously learn optimal contrastive pairs. To address these limitations, we propose a novel approach named Semantic Retrieval Augmented Contrastive Learning (SRA-CL). SRA-CL leverages the semantic understanding and reasoning capabilities of LLMs to generate expressive embeddings that capture both user preferences and item characteristics. These semantic embeddings enable the construction of candidate pools for inter-user and intra-user contrastive learning through semantic-based retrieval. To further enhance the quality of the contrastive samples, we introduce a learnable sample synthesizer that optimizes the contrastive sample generation process during model training. SRA-CL adopts a plug-and-play design, enabling seamless integration with existing sequential recommendation architectures. Extensive experiments on four public datasets demonstrate the effectiveness and model-agnostic nature of our approach.