Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution. The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index. By indexing items in a tree hierarchy and training a user-node preference prediction model satisfying a max-heap like property in the tree, TDM provides logarithmic computational complexity w.r.t. the corpus size, enabling the use of arbitrary advanced models in candidate retrieval and recommendation.

Thanks to their scalability, two-stage recommenders are used by many of today's largest online platforms, including YouTube, LinkedIn, and Pinterest. These systems produce recommendations in two steps: (i) multiple nominators--tuned for low prediction latency--preselect a small subset of candidates from the whole item pool; (ii) a slower but more accurate ranker further narrows down the nominated items, and serves to the user. Despite their popularity, the literature on two-stage recommenders is relatively scarce, and the algorithms are often treated as mere sums of their parts. Such treatment presupposes that the two-stage performance is explained by the behavior of the individual components in isolation. This is not the case: using synthetic and real-world data, we demonstrate that interactions between the ranker and the nominators substantially affect the overall performance.

We consider running multiple instances of multi-armed bandit (MAB) problems in parallel. A main motivation for this study are online recommendation systems, in which each of N users is associated with a MAB problem and the goal is to exploit users' similarity in order to learn users' preferences to K items more efficiently. We consider the adversarial MAB setting, whereby an adversary is free to choose which user and which loss to present to the learner during the learning process. Users are in a social network and the learner is aided by a-priori knowledge of the strengths of the social links between all pairs of users. It is assumed that if the social link between two users is strong then they tend to share the same action. The regret is measured relative to an arbitrary function which maps users to actions.

Plenty of the tech we cover costs less than 50. And some gadgets hovering close enough to that price just need a decent discount to put them in range. During Amazon's Prime Day sale, plenty of tech deals can be had for under 50 and we've gathered up the best of what's out there. If you need to pick up microSD cards, power banks, digital streamers or even a smart speaker, now's the time. As always, these Prime Day picks are drawn from our own testing, coverage and reviews.

In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization. This connection not only improves the running time for constrained submodular maximization but also provides the state of the art guarantee. More precisely, for maximizing a monotone submodular function subject to the combination of a k -matchoid and \ell -knapsack constraints (for \ell\leq k), we propose a potential function that can be approximately minimized. Once we minimize the potential function up to an \epsilon error, it is guaranteed that we have found a feasible set with a 2(k 1 \epsilon) -approximation factor which can indeed be further improved to (k 1 \epsilon) by an enumeration technique. We extensively evaluate the performance of our proposed algorithm over several real-world applications, including a movie recommendation system, summarization tasks for YouTube videos, Twitter feeds and Yelp business locations, and a set cover problem.

Amazon's fall Prime Day sale--also known as Big Deals Days--ends tonight. It's October, yes, but it's never too early to jump on that holiday gift shopping. We've combed through the deals and found the best ones, based on our years of testing and reviewing. WIRED's picks for the best Prime Day deals only include products someone from our team has personally tested and reviewed. We track prices using several tools to avoid falling for fake discounts. There are no shoddy knockoffs or overpriced products among our recommendations, just good deals on good stuff. We've linked our reviews and buying guide throughout to help you make fully informed buying decisions. We test products year-round and handpicked these Prime Day deals. We'll update this guide regularly throughout Prime Day by adding fresh deals and removing dead deals. This is our favorite e-reader. You'll have the choice between the base Paperwhite and the Signature Edition (8/10, WIRED Recommends), which comes with 16 gigabytes ...

Collaborative filtering has been widely used in recommender systems. Existing work has primarily focused on improving the prediction accuracy mainly via either building refined models or incorporating additional side information, yet has largely ignored the inherent distribution of the input rating data. In this paper, we propose a data debugging framework to identify overly personalized ratings whose existence degrades the performance of a given collaborative filtering model. The key idea of the proposed approach is to search for a small set of ratings whose editing (e.g., modification or deletion) would near-optimally improve the recommendation accuracy of a validation set. Experimental results demonstrate that the proposed approach can significantly improve the recommendation accuracy.

We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contributions to all levels of the embodied AI stack – data, simulation, and benchmark tasks. These large-scale engineering contributions allow us to systematically compare deep reinforcement learning (RL) at scale and classical sense-plan-act (SPA) pipelines in long-horizon structured tasks, with an emphasis on generalization to new objects, receptacles, and layouts. We find that (1) flat RL policies struggle on HAB compared to hierarchical ones; (2) a hierarchy with independent skills suffers from'hand-off problems', and (3) SPA pipelines are more brittle than RL policies.

Best Buy's 48-Hour Flash Sale has a large selection of TVs, gaming devices and laptops marked down. Get the holiday season started with a jump start on savings during Best Buy's 2024 48-Hour Flash Sale, which begins today and runs through Oct. 9. The sale features top deals on TVs, gaming devices, laptops, monitors and more. Not only are these items often the most wanted gift on a wish list, but they're also huge in helping people host and celebrate throughout the season. This HP is a workhorse laptop and its 200 off. Hewlett Packard's 15.6-inch touch-screen laptop with Intel Core i3 and 8 gigabytes of memory can handle everyday tasks smoothly and efficiently.

Big-ticket tech items get most of the attention during any Amazon Prime Day sale. But here, we're checking out the smaller supporting characters that make our headlining gear work better -- like chargers, storage cards, cables, batteries and the like. Many of the deals we've found on the gear we've tested and recommend are currently going for less than 50, so we've rounded them up here, separated by price point. It's proof that you don't have to spend a ton to save during this sale. Here are the best Prime Day Tech deals under 50.