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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.

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We explore a novel problem in streaming submodular maximization, inspired by the dynamics of news-recommendation platforms. We consider a setting where users can visit a news website at any time, and upon each visit, the website must display up to k news items. User interactions are inherently stochastic: each news item presented to the user is consumed with a certain acceptance probability by the user, and each news item covers certain topics. Our goal is to design a streaming algorithm that maximizes the expected total topic coverage. To address this problem, we establish a connection to submodular maximization subject to a matroid constraint.

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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.

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We've gone from A to Z to find Amazon's best Prime Day deals on the gear worth owning. Amazon Prime Day is here once again. Amazon's annual Prime Day deals aims to entice us with an endless scroll of "discounts (some real, many fake), hammering away with red slashes, big percentages off, and coupons you can only see after adding to cart. While Prime Day deals aren't what they once were--its success has inspired a massive number of fake deals and attracted obscure brands--there are still some very significant discounts to be found. For the next four days, the WIRED Reviews team will be pooling our hundreds of years of collective expertise to find actual savings on products we have personally tested and approved. Let us absorb the neon signage and "buy now" buttons on your behalf and share the deals worth sharing. We'll keep this list updated frequently for the duration of the sale, which runs from June 23 to June 26. This is our very favorite MagSafe power bank . Wireless and MagSafe charging aren't always the fastest or most efficient, but despite its bulk, this large-capacity bank can top off modern phones once (or maybe a little more than that) without overheating or taking forever. One of the best budget wireless chargers is even more affordable thanks to Prime Day. You can buy fancier, faster wireless chargers, but if you just want a simple option that'll top off your phone, this is worth checking out. It can deliver up to 10 watts, though you'll need to supply your own wall adapter. Want something that can fast charge your phone, juice up your tablet, and even refill your laptop? This generous 25,000-mAh capacity can do it all, but stops shy of the carry-on air travel limit. The maximum output is 165 watts for two devices, but 100 watts for a single device. It has lovely rounded edges, a retractable, flat, 2.3-foot USB-C cable on the top, and a snazzy, durable, braided 1-foot USB-C cable that doubles as a carry loop. This remains one of my favorite Windows laptops, despite the recent price increases. But now, it's unexpectedly dropped to $835 for Prime Day, making it the best laptop Prime Day deal I've found so far. Price aside, though, my favorite feature is the 3:2 aspect ratio screen, which also has a faster 120-Hz refresh rate. It's absolutely gorgeous, and all the extra vertical screen space gives more room to work with. A new version just got announced with a more powerful Snapdragon X2 chip inside, but it's considerably more expensive . Unlike so many Windows laptops around $500, the OmniBook 3 has excellent performance and battery life. And while the touchpad isn't the best, the specs alone make it the very best cheap laptop you can buy.