This easy-to-use, Wi-Fi-enabled bean-to-cup brewer is good, but not quite great. App is simple and works well. "Smart" features only work with Amazon Alexa and Google Assistant. Integrating with HomeKit via third-party apps is not worth the effort. Pricey for what's essentially an auto-drip machine that works with an app, which is no longer novel or futuristic.

When you purchase through links in our articles, we may earn a small commission. The TP-Link Tapo S505 smart light switch is on sale at Amazon. Grab this 2-pack for just $20 while the deal lasts. My smart home life became so much better once I swapped out my old light switches for Tapo ones. The benefits were many, including that I no longer had to get off the couch to turn off the lights--great in the winter when I was already cozy under the blankets!

The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

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When you purchase through links in our articles, we may earn a small commission. Amazon's Echo Spot speaker gets its first discount this year: 38% off You can get the Echo Spot for just $50 (was $80) right now, close to the best price it's ever been. We're days away from Amazon's Big Spring Sale event and we're already seeing some impressive discounts, especially for the company's own Echo line. For instance, the Echo Spot is down to $50 (was $80). That's the first time it's gone on sale this year and just a few bucks away from its best-ever price.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

The sparse matrix estimation problem consists of estimating the distribution of an $n\times n$ matrix $Y$, from a sparsely observed single instance of this matrix where the entries of $Y$ are independent random variables. This captures a wide array of problems; special instances include matrix completion in the context of recommendation systems, graphon estimation, and community detection in (mixed membership) stochastic block models. Inspired by classical collaborative filtering for recommendation systems, we propose a novel iterative, collaborative filtering-style algorithm for matrix estimation in this generic setting. We show that the mean squared error (MSE) of our estimator converges to $0$ at the rate of $O(d^2 (pn)^{-2/5})$ as long as $\omega(d^5 n)$ random entries from a total of $n^2$ entries of $Y$ are observed (uniformly sampled), $\E[Y]$ has rank $d$, and the entries of $Y$ have bounded support. The maximum squared error across all entries converges to $0$ with high probability as long as we observe a little more, $\Omega(d^5 n \ln^5(n))$ entries. Our results are the best known sample complexity results in this generality.

When you purchase through links in our articles, we may earn a small commission. Amazon's Echo Dot Max just got its best discount yet (25% off) It's the newest model of the Echo Dot and it can be yours for just $75 (was $100) right now with this limited-time Amazon deal. Amazon's newest smart speaker, the Echo Dot Max, has just gone on sale for the best price it's had so far. The latest model of the Echo Dot, this one's a bit larger and a lot louder than previous iterations, plus it now features a new chip that's perfectly optimized for the Alexa Plus AI assistant. In this way, the speaker ensures an effortless experience across all major streaming platforms.

Low-rank matrix approximation (LRMA) methods have achieved excellent accuracy among today's collaborative filtering (CF) methods. In existing LRMA methods, the rank of user/item feature matrices is typically fixed, i.e., the same rank is adopted to describe all users/items. However, our studies show that submatrices with different ranks could coexist in the same user-item rating matrix, so that approximations with fixed ranks cannot perfectly describe the internal structures of the rating matrix, therefore leading to inferior recommendation accuracy. In this paper, a mixture-rank matrix approximation (MRMA) method is proposed, in which user-item ratings can be characterized by a mixture of LRMA models with different ranks. Meanwhile, a learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to MRMA. Experimental studies on MovieLens and Netflix datasets demonstrate that MRMA can outperform six state-of-the-art LRMA-based CF methods in terms of recommendation accuracy.

Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences. Recommender systems then use this information to make personalized suggestions to users. When users accept these recommendations it creates a feedback loop in the recommender system, and these loops iteratively influence the collaborative filtering algorithm's predictions over time. We investigate whether it is possible to identify items affected by these feedback loops. We state sufficient assumptions to deconvolve the feedback loops while keeping the inverse solution tractable. We furthermore develop a metric to unravel the recommender system's influence on the entire user-item rating matrix. We use this metric on synthetic and real-world datasets to (1) identify the extent to which the recommender system affects the final rating matrix, (2) rank frequently recommended items, and (3) distinguish whether a user's rated item was recommended or an intrinsic preference. Our results indicate that it is possible to recover the ratings matrix of intrinsic user preferences using a single snapshot of the ratings matrix without any temporal information.