Eugenia Kuyda defended AI companion bots during an interview with Fox News Digital and argued that dating app Replika is just one of many possible solutions to loneliness. AI, or artificial intelligence, is a branch of computer science that is designed to understand and store human intelligence, mimic human capabilities including the completion of tasks, process human language and perform speech recognition. AI is the leading innovation in technology today and its primary goal is to eliminate tedious tasks and assist in immediately accessing extremely detailed and hyper-focused information and data. AI has the ability to consume and process massive datasets and develop patterns to make predictions for the completion of future tasks. While the interest in AI around the world is growing, the science poses an existential crisis for jobs, companies, whole industries and potentially human existence.

Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight model on a server. Moreover, we propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items. The overall learning process is formulated into a unified federated optimization framework. Specifically, unlike previous methods that share exactly the same item embeddings across users in a federated system, dual personalization allows mild finetuning of item embeddings for each user to generate user-specific views for item representations which can be integrated into existing federated recommendation methods to gain improvements immediately. Experiments on multiple benchmark datasets have demonstrated the effectiveness of PFedRec and the dual personalization mechanism. Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings. The code is available.

Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. Many of them benefit from utilizing users' behavior sequences as plain texts, representing rich information in any domain or system without losing generality. Hence, a question arises: Can language modeling for user history corpus help improve recommender systems? While its versatile usability has been widely investigated in many domains, its applications to recommender systems still remain underexplored. We show that language modeling applied directly to task-specific user histories achieves excellent results on diverse recommendation tasks. Also, leveraging additional task-agnostic user histories delivers significant performance benefits. We further demonstrate that our approach can provide promising transfer learning capabilities for a broad spectrum of real-world recommender systems, even on unseen domains and services.

The biggest news in tech this week came from Google's annual developer conference on Wednesday. They announced three new devices: The Pixel 7a smartphone, the Pixel Tablet and the Pixel Fold. Discounts on brand new products don't happen often, but both Amazon and Google were quick to bundle Pixel 7a orders with a $50 Amazon gift card, or a free pair of Pixel Buds, respectively -- not sales per se, but free stuff is still compelling for anyone already planning on getting a new phone. Of course, there were deals unrelated to Google too, like savings on Amazon devices including the Fire TV Stick 4K Max, Echo speakers, and nearly all Kindle models. Here are the best tech deals from this week that you can still get today.

Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extract universal item representation via pre-trained language models (PLMs), they ignore the crucial item relationships. This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs. We identify three challenges for pre-training PKG, which are multi-type relations in PKG, semantic divergence between item generic information and relations and domain discrepancy from PKG to downstream ZSIR task. We address the challenges by proposing four pre-training tasks and novel task-oriented adaptation (ToA) layers. Moreover, this paper discusses how to fine-tune the model on new recommendation task such that the ToA layers are adapted to ZSIR task. Comprehensive experiments on 18 markets dataset are conducted to verify the effectiveness of the proposed model in both knowledge prediction and ZSIR task.

We develop a novel latent-bandit algorithm for tackling the cold-start problem for new users joining a recommender system. This new algorithm significantly outperforms the state of the art, simultaneously achieving both higher accuracy and lower regret.

Twitter users are calling on Elon Musk to develop an AI-powered dating app called'Twinder,' touting it as the way'to save humanity from extinction.' The idea came after Musk replied'population collapse' to a tweet showing how fertility rates keep dropping in the Nordic countries. The potential dating app, which the Twitter CEO deemed an'interesting idea,' would use artificial intelligence to make matches instead of random swiping. The suggested service would feed AI Twitter accounts, including posts, comments and likes, and the technology would look for another user with similar behaviors and interests. The Twitter thread, viewed over two million times, has hundreds of comments, with some sharing how they met their partner on the social network.

Amazon's Echo speakers, and the Alexa assistant, are incredibly useful and pretty darn invasive. On the practical side, it can function as a security alarm with a device you already own. Here's how Alexa Guard works. I once found a voice recording of a conversation my Echo caught when I knew for sure I didn't ask Alexa to listen in. It just thought it heard the wake word.

Walden University students are actively using three AI tools, Linda, Charlotte and Julian to set themselves up for educational success. A Minnesota university is actively using several unique artificial intelligence (AI) models to help tutor students, complete assignments and bolster their verbal and non-verbal communication skills. Adtalem Chief Customer Officer Steve Tom has helped to deploy three distinct AI systems: Charlotte, Linda, and Julian at Walden University. The tools help counseling students prepare for their careers by working with "digital people" to cultivate communication and crisis management skills. Charlotte is a digital assistant chatbot that can help students stay on top of tasks and assignments to navigate a class curriculum efficiently.

Meta is not pivoting away from its signature product, the metaverse. Or at least that's what the Meta chief executive, Mark Zuckerberg, is arguing. Despite reports that sales teams at Meta have spent less time pitching the metaverse to advertisers, Zuckerberg claimed on the tech firm's latest quarterly earnings call that it's business as usual over at the company formerly known as Facebook. "A narrative has developed that we're somehow moving away from focusing on the metaverse vision, so I just want to say upfront that that's not accurate," the CEO said. But neither is the virtual reality world the only product Meta has bet its future on, Zuckerberg argued: "We've been focusing on both AI and the metaverse for years now, and we will continue to focus on both."