Amazon Prime Day covers deals across every department but at Engadget, we focus mainly on discounts on the tech we've tested and recommend. As you may notice when browsing day two of Amazon's sale, a lot of the tech gear is still pretty pricey -- even after the discounts. So, as an antidote, we went through every tech deal out there and found the affordable Prime Day deals on the accessories and smaller gadgets that we recommend. Here are the best Prime Day tech deals under 50. As with all Engadget tech deals coverage, we only highlight discounts on gear we've tested or have otherwise used and know to be worthy of your money. We cross-checked our guides and reviews with the Prime Day deals Amazon has put forth to come up with what you see here. The Anker Nano power bank in black is on sale for Prime Day for 16.13. That's a 15 percent discount and a good deal for one of the best power banks we tested. We like the foldable USB-C connector which means you don't have to remember a separate cable and the amount of charge it delivers for such a small package.

Don't fret if you missed out on day one of Amazon Prime Day -- there are still tons of deals to be had during the final day today. Prime Day deals have discounted plenty of our favorites this year, including a bunch of affordable tech that's now even cheaper thanks to the shopping event. We've sifted through all of the junk to find all of the tech deals under 25 that are actually worth your money. As a reminder (and for the uninitiated): Engadget treats tech deals with the same care as we would "regular" tech news. When we scour the web for deals, we're looking not only for the best prices possible, but also the best products as well.

Cruise around TikTok these days and you're bound to stumble on one: A young woman, standing next to a presumed beau, pointing out their "feminine, vintage-Levi's-wearing, tote-bag-carrying, mustached little boyfriend." If you haven't seen one of those, maybe it's the person saying "if my friends think you're a little bit fruity" then you're their type. Keep scrolling and you'll see commenters saying they're "manifesting my little gay boyfriend." Earlier this month, a piece in Dazed explained the trend thus: "Fruity boys" are "the new soft boys." The slang term, the latest in a long line of similar monikers going back to "metrosexual," aims to identify a new archetype: a man who is usually straight who possesses a set of nebulously feminine or queerish qualities, or a "zesty aura."

Millions of Americans are currently dealing with a heat wave across the US, so staying cool is important. There are many ways to deal with the scorching heat, and the good news is that quite a few of the tools that can help are on sale for Prime Day. These range from the obvious Prime Day air conditioner and tower fan deals, but you can also find discounted cooling sheets and even a mattress that can help transfer your body heat during the night for better sleep if you are in fact sleeping and not obsessively tracking our Prime Day liveblog. We test products year-round and handpicked these deals. We'll update this guide periodically throughout the sale event.

WIRED's coverage of the best Amazon Prime Day deals and biggest discounts is, as they say, built different. For starters, we only include products someone from our team has personally tested and reviewed. That means you will not find flimsy fad gadgets or shoddy dupes among our recommendations. What remains is all solid stuff. You'll often find a link to a longer write-up to a review or buying guide if you want to make a fully informed buying decision. Additionally, we obsessively track prices to make sure everything on the list is a genuinely good price right now. For more on that, consult our helpful guide to shopping like a pro on Prime Day. Today is the last day of Prime Day, so you might not see some of these deals until Amazon's second Prime Day event in October or Black Friday in November. We test products year-round and handpicked these Prime Day deals. Products that are sold out or no longer discounted will be crossed out. We'll update this guide regularly throughout Prime ...

Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem. While CDR offers substantial potential for enhancing recommendation performance, most existing CDR methods suffer from sensitivity to the ratio of overlapping users and intrinsic discrepancy between source and target domains. To overcome these limitations, in this work, we explore the application of graph signal processing (GSP) in CDR scenarios. We propose CGSP, a unified CDR framework based on GSP, which employs a cross-domain similarity graph constructed by flexibly combining target-only similarity and source-bridged similarity. By processing personalized graph signals computed for users from either the source or target domain, our framework effectively supports both inter-domain and intra-domain recommendations. Our empirical evaluation demonstrates that CGSP consistently outperforms various encoder-based CDR approaches in both intra-domain and inter-domain recommendation scenarios, especially when the ratio of overlapping users is low, highlighting its significant practical implication in real-world applications.

Recent years have witnessed a rapid growth of recommender systems, providing suggestions in numerous applications with potentially high social impact, such as health or justice. Meanwhile, in Europe, the upcoming AI Act mentions \emph{transparency} as a requirement for critical AI systems in order to ``mitigate the risks to fundamental rights''. Post-hoc explanations seamlessly align with this goal and extensive literature on the subject produced several forms of such objects, graphs being one of them. Early studies in visualization demonstrated the graphs' ability to improve user understanding, positioning them as potentially ideal explanations. However, it remains unclear how graph-based explanations compare to other explanation designs. In this work, we aim to determine the effectiveness of graph-based explanations in improving users' perception of AI-based recommendations using a mixed-methods approach. We first conduct a qualitative study to collect users' requirements for graph explanations. We then run a larger quantitative study in which we evaluate the influence of various explanation designs, including enhanced graph-based ones, on aspects such as understanding, usability and curiosity toward the AI system. We find that users perceive graph-based explanations as more usable than designs involving feature importance. However, we also reveal that textual explanations lead to higher objective understanding than graph-based designs. Most importantly, we highlight the strong contrast between participants' expressed preferences for graph design and their actual ratings using it, which are lower compared to textual design. These findings imply that meeting stakeholders' expressed preferences might not alone guarantee ``good'' explanations. Therefore, crafting hybrid designs successfully balancing social expectations with downstream performance emerges as a significant challenge.

Multimodal recommendation systems (MMRS) have received considerable attention from the research community due to their ability to jointly utilize information from user behavior and product images and text. Previous research has two main issues. First, many long-tail items in recommendation systems have limited interaction data, making it difficult to learn comprehensive and informative representations. However, past MMRS studies have overlooked this issue. Secondly, users' modality preferences are crucial to their behavior. However, previous research has primarily focused on learning item modality representations, while user modality representations have remained relatively simplistic.To address these challenges, we propose a novel Graphs and User Modalities Enhancement (GUME) for long-tail multimodal recommendation. Specifically, we first enhance the user-item graph using multimodal similarity between items. This improves the connectivity of long-tail items and helps them learn high-quality representations through graph propagation. Then, we construct two types of user modalities: explicit interaction features and extended interest features. By using the user modality enhancement strategy to maximize mutual information between these two features, we improve the generalization ability of user modality representations. Additionally, we design an alignment strategy for modality data to remove noise from both internal and external perspectives. Extensive experiments on four publicly available datasets demonstrate the effectiveness of our approach.

Continual learning(CL) is a useful technique to acquire dynamic knowledge continually. Although powerful cloud platforms can fully exert the ability of CL,e.g., customized recommendation systems, similar personalized requirements for edge devices are almost disregarded. This phenomenon stems from the huge resource overhead involved in training neural networks and overcoming the forgetting problem of CL. This paper focuses on these scenarios and proposes a compact algorithm called LightCL. Different from other CL methods bringing huge resource consumption to acquire generalizability among all tasks for delaying forgetting, LightCL compress the resource consumption of already generalized components in neural networks and uses a few extra resources to improve memory in other parts. We first propose two new metrics of learning plasticity and memory stability to seek generalizability during CL. Based on the discovery that lower and middle layers have more generalizability and deeper layers are opposite, we $\textit{Maintain Generalizability}$ by freezing the lower and middle layers. Then, we $\textit{Memorize Feature Patterns}$ to stabilize the feature extracting patterns of previous tasks to improve generalizability in deeper layers. In the experimental comparison, LightCL outperforms other SOTA methods in delaying forgetting and reduces at most $\textbf{6.16$\times$}$ memory footprint, proving the excellent performance of LightCL in efficiency. We also evaluate the efficiency of our method on an edge device, the Jetson Nano, which further proves our method's practical effectiveness.

In Reinforcement Learning-based Recommender Systems (RLRS), the complexity and dynamism of user interactions often result in high-dimensional and noisy state spaces, making it challenging to discern which aspects of the state are truly influential in driving the decision-making process. This issue is exacerbated by the evolving nature of user preferences and behaviors, requiring the recommender system to adaptively focus on the most relevant information for decision-making while preserving generaliability. To tackle this problem, we introduce an innovative causal approach for decomposing the state and extracting \textbf{C}ausal-\textbf{I}n\textbf{D}ispensable \textbf{S}tate Representations (CIDS) in RLRS. Our method concentrates on identifying the \textbf{D}irectly \textbf{A}ction-\textbf{I}nfluenced \textbf{S}tate Variables (DAIS) and \textbf{A}ction-\textbf{I}nfluence \textbf{A}ncestors (AIA), which are essential for making effective recommendations. By leveraging conditional mutual information, we develop a framework that not only discerns the causal relationships within the generative process but also isolates critical state variables from the typically dense and high-dimensional state representations. We provide theoretical evidence for the identifiability of these variables. Then, by making use of the identified causal relationship, we construct causal-indispensable state representations, enabling the training of policies over a more advantageous subset of the agent's state space. We demonstrate the efficacy of our approach through extensive experiments, showcasing our method outperforms state-of-the-art methods.