This is one of your best chances to set up a smart home on a budget, or expand the one you have. Amazon is bundling the latest-generation Echo Dot with a Kasa Smart Plug Mini for only $28, or a whopping $45 off. The 2022 Echo Dot is our favorite budget smart speaker for a good reason: it sounds better than you'd expect at its regular price, let alone on sale. It's loud enough to fill a large room, and clear enough to do justice to your music. The Alexa ecosystem is also robust, so you won't have problems finding services and smart home devices (including the smart plug, of course) you can control with your voice.

Google released its redesigned Home app last month, adding routines to give users more control over smart home automations. Now, it's introducing a new script editor, the company announced in a Google Nest blog post. It gives users even more granular control over automations, letting them do things like "dim lights and lower blinds when the living room TV is on after dark," to cite one Google example. It does require some basic programming abilities, though, as it uses the YAML data serialization language. Building an automation requires three elements: starters, conditions and actions.

Information access systems, such as search engines, recommender systems, and conversational assistants, have become integral to our daily lives as they help us satisfy our information needs. However, evaluating the effectiveness of these systems presents a long-standing and complex scientific challenge. This challenge is rooted in the difficulty of assessing a system's overall effectiveness in assisting users to complete tasks through interactive support, and further exacerbated by the substantial variation in user behaviour and preferences. To address this challenge, user simulation emerges as a promising solution. This book focuses on providing a thorough understanding of user simulation techniques designed specifically for evaluation purposes. We begin with a background of information access system evaluation and explore the diverse applications of user simulation. Subsequently, we systematically review the major research progress in user simulation, covering both general frameworks for designing user simulators, utilizing user simulation for evaluation, and specific models and algorithms for simulating user interactions with search engines, recommender systems, and conversational assistants. Realizing that user simulation is an interdisciplinary research topic, whenever possible, we attempt to establish connections with related fields, including machine learning, dialogue systems, user modeling, and economics. We end the book with a detailed discussion of important future research directions, many of which extend beyond the evaluation of information access systems and are expected to have broader impact on how to evaluate interactive intelligent systems in general.

For content recommender systems such as TikTok and YouTube, the platform's decision algorithm shapes the incentives of content producers, including how much effort the content producers invest in the quality of their content. Many platforms employ online learning, which creates intertemporal incentives, since content produced today affects recommendations of future content. In this paper, we study the incentives arising from online learning, analyzing the quality of content produced at a Nash equilibrium. We show that classical online learning algorithms, such as Hedge and EXP3, unfortunately incentivize producers to create low-quality content. In particular, the quality of content is upper bounded in terms of the learning rate and approaches zero for typical learning rate schedules. Motivated by this negative result, we design a different learning algorithm -- based on punishing producers who create low-quality content -- that correctly incentivizes producers to create high-quality content. At a conceptual level, our work illustrates the unintended impact that a platform's learning algorithm can have on content quality and opens the door towards designing platform learning algorithms that incentivize the creation of high-quality content.

An alternative to stratified is parallel integration. In contrast to stratified frameworks, parallel integration applies in settings Parallel neurosymbolic architectures have been in which the same task can be solved both symbolically applied effectively in NLP by distilling knowledge and sub-symbolically and the aim is to increase the accuracy from a logic theory into a deep model. However, of the end task by distilling knowledge from the logic prior art faces several limitations including component into the neural one and vice versa. Two parallel supporting restricted forms of logic theories and neurosymbolic frameworks have been proposed recently: relying on the assumption of independence between Teacher-Student (T-S) by Hu et al. (Hu et al., 2016a;b) and the logic and the deep network.

Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance in recommendation with their data augmentation schemes, aiming at dealing with highly sparse data. Despite their success, most existing graph contrastive learning methods either perform stochastic augmentation (e.g., node/edge perturbation) on the user-item interaction graph, or rely on the heuristic-based augmentation techniques (e.g., user clustering) for generating contrastive views. We argue that these methods cannot well preserve the intrinsic semantic structures and are easily biased by the noise perturbation. In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders. Our model exclusively utilizes singular value decomposition for contrastive augmentation, which enables the unconstrained structural refinement with global collaborative relation modeling. Experiments conducted on several benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the superiority of LightGCL's robustness against data sparsity and popularity bias. The source code of our model is available at https://github.com/HKUDS/LightGCL.

Given a user's input text, text-matching recommender systems output relevant items by comparing the input text to available items' description, such as product-to-product recommendation on e-commerce platforms. As users' interests and item inventory are expected to change, it is important for a text-matching system to generalize to data shifts, a task known as out-of-distribution (OOD) generalization. However, we find that the popular approach of fine-tuning a large, base language model on paired item relevance data (e.g., user clicks) can be counter-productive for OOD generalization. For a product recommendation task, fine-tuning obtains worse accuracy than the base model when recommending items in a new category or for a future time period. To explain this generalization failure, we consider an intervention-based importance metric, which shows that a fine-tuned model captures spurious correlations and fails to learn the causal features that determine the relevance between any two text inputs. Moreover, standard methods for causal regularization do not apply in this setting, because unlike in images, there exist no universally spurious features in a text-matching task (the same token may be spurious or causal depending on the text it is being matched to). For OOD generalization on text inputs, therefore, we highlight a different goal: avoiding high importance scores for certain features. We do so using an intervention-based regularizer that constraints the causal effect of any token on the model's relevance score to be similar to the base model. Results on Amazon product and 3 question recommendation datasets show that our proposed regularizer improves generalization for both in-distribution and OOD evaluation, especially in difficult scenarios when the base model is not accurate.

Amazon Web Services has been hit with a worldwide outage, impacting dozens of websites that use the company's cloud hosting service. DownDetector, which monitors online outages, shows hundreds of thousands of issue reports from around the globe. Amazon Web Services began experiencing problems around 2:56 pm ET, taking out other websites like IMDB, McDonald's and OkCupid. The e-commerce giant's purchasing platform, music and virtual assistant Alexa are also experiencing problems. Amazon Web Services has been hit with a worldwide outage impacting hundreds of websites that use the company's cloud-hosting service Reports first indicated Amazon Web Services (AWS) was experiencing issues, but other websites began to follow one by one.

Smart plugs are among the simpler smart home devices, giving you voice and app control over appliances like lamps, fans, humidifiers and basic coffee makers. You can create schedules and routines, too, either through a plug's proprietary app or through your preferred smart home platform. But much like other IoT devices, which system plays nice with which plug depends on compatibility, and each brand's app offers different features. We tested out ten popular options to see which are worth buying. Before you buy one, it helps to know what a smart plug can and can't do. They work best with things that have an on/off switch, making them great for lamps and other lights.

Amazon's Echo Studio usually sells for $200 and rarely goes on sale, but you can get the supersize Alexa hub right now in Charcoal or Glacier for $160. We called the Echo Studio the "best sounding speaker Amazon has built" in our review when it was released in 2019. The company has released more speakers and smart displays since then, but the Echo Studio remains a solid option for immersive, high-resolution music streaming. Amazon's Echo Studio speaker is on sale for $160. We gave the Echo Studio a score of 88 in our review and praised it for having excellent audio quality best demonstrated by high-definition and Ultra HD tracks.