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. The smoothness of the function is captured by a resistance-based dispersion measure $\Psi$. We present two learning algorithms, GABA-I and GABA-II, which exploit the network structure to bias towards functions of low $\Psi$ values.

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user.

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. Furthermore, we observe that the identified ratings significantly deviate from the average ratings of the corresponding items, and the proposed approach tends to modify them towards the average.

LG TVs add'delete' option for Copilot The transition from Assistant to Gemini will continue into 2026. Google wanted to remove Assistant from most Android phones by the end of 2025 and replace it with Gemini. But now the company has announced that it needs a bit more time to make its AI assistant the new default digital helper for most of its users. Google said that it's adjusting its previously announced timeline to make sure [it delivers] a seamless transition and that updates to convert Assistant to Gemini on Android devices will continue into the next year. The company also said that it's sharing more details in the coming months, so it's possible that the transition will go past early 2026. Assistant's retirement was pretty much expected the moment Google launched Gemini and started giving it Assistant's capabilities, such as the ability to control smart devices connected to your phone.

Warner Bros. rejects Paramount's hostile bid But will it ask them to buy stuff? Amazon just introduced a . This lets Alexa+ answer the doorbell and converse with visitors, which certainly sounds futuristic in a gated community as dystopia kind of way. There are several caveats here. First of all, it only works with certain newer Ring video doorbell models.

Amazon has launched a web portal for Alexa+ at Alexa.com, providing early access users with a ChatGPT-like interface featuring suggested prompts and easy text copying capabilities. PCWorld reports the portal allows file uploads for document analysis and offers improved usability compared to previous voice-only interactions with Amazon's AI assistant. Alexa+ remains free during early access, with future plans to offer it free to Prime members while charging non-Prime users a monthly fee. It's been nearly a year since Amazon first launched the new, AI-enhanced Alexa+, but until now, a key feature has been missing: the ability to access and chat with Alexa+ on the web. Now it appears Amazon has fulfilled that promise, with an Alexa+ web portal finally going live--for at least some Alexa+ early access users, anyway--at Alexa.com.

Kindle's in-book AI assistant can answer all your questions without spoilers But the catch is authors and publishers can't opt out of having this feature in their works. If you're several chapters into a novel and forgot who a character was, Amazon is hoping its new Kindle feature will jog your memory without ever having to put the e-reader down. This feature, called Ask this Book, was announced during Amazon's hardware event in September, but is finally available for US users on the Kindle iOS app. According to Amazon, the feature can currently be found on thousands of English best-selling Kindle titles and only reveals information up to your current reading position for spoiler-free responses. To use it, you can highlight a passage in any book you've bought or borrowed and ask it questions about plot, characters or other crucial details, and the AI assistant will offer immediate, contextual, spoiler-free information.

I thought I'd struck lucky on a dating app but invited a monster into my life Handsome, charming, a gentle giant - Katie Yates believed Jason Smith was a real catch after meeting him on a dating app. But within months he had subjected her to relentless physical and mental abuse before raping her and attempting to drown her in the bath just before Christmas. Katie, 42 and from Cardiff, has waived her anonymity as a victim of sexual assault to warn women to be wary of strangers they meet on dating apps who may pose as nice guys in an attempt to lure them in. You scroll on all the profiles with smiling photos and slick words but there are some people who should be looking for a therapist, not a girlfriend, she said. Katie had been single for five years when she signed up to a dating app in February 2018.

When you purchase through links in our articles, we may earn a small commission. I switched to Gemini for Home. Here's the Google Assistant feature I miss You'll need to pay up to get this key Google Assistant feature back. I recently made the switch to Gemini on my Google smart speakers and displays, and for the most part, I'm liking it. Gemini is chatty without being a blabbermouth, and it capably controls my smart home while also delivering detailed weather reports and answers to my other queries.

Studying how recommendation systems reshape social networks is difficult on live platforms: confounds abound, and controlled experiments risk user harm. We present an agent-based simulator where content production, tie formation, and a graph attention network (GAT) recommender co-evolve in a closed loop. We calibrate parameters using Mastodon data and validate out-of-sample against Bluesky (4--6\% error on structural metrics; 10--15\% on held-out temporal splits). Across 18 configurations at 100 agents, we find that \emph{activation timing} affects outcomes: introducing recommendations at $t=10$ vs.\ $t=40$ decreases transitivity by 10\% while engagement differs by $<$8\%. Delaying activation increases content diversity by 9\% while reducing modularity by 4\%. Scaling experiments ($n$ up to 5,000) show the effect persists but attenuates. Jacobian analysis confirms local stability under bounded reactance parameters. We release configuration schemas and reproduction scripts.