If you use Instagram, it's likely you've spotted a few strange changes to some of your friends' bios over the last few weeks. Thousands of users have added a pear emoji to the description on their profile - and there's a simple explanation as to why. The emoji is a new way for singletons to quietly indicate their relationship status. The idea is the brainchild of Pear - a dating concept that describes itself as'the world's biggest social experiment.' Here's everything you need to know, including what the emoji means and how you can use it in your profile.

In high performance systems it is sometimes hard to build very large graphs that are efficient both with respect to memory and compute. This paper proposes a data structure called Markov-chain-priority-queue (MCPrioQ), which is a lock-free sparse markov-chain that enables online and continuous learning with time-complexity of $O(1)$ for updates and $O(CDF^{-1}(t))$ inference. MCPrioQ is especially suitable for recommender-systems for lookups of $n$-items in descending probability order. The concurrent updates are achieved using hash-tables and atomic instructions and the lookups are achieved through a novel priority-queue which allows for approximately correct results even during concurrent updates. The approximatly correct and lock-free property is maintained by a read-copy-update scheme, but where the semantics have been slightly updated to allow for swap of elements rather than the traditional pop-insert scheme.

As some of you might know, I'm a runner. On occasion I review sports watches, and outside of work I'm a certified marathon coach. So when Engadget wanted to round up the best wireless workout headphones, I raised my hand. In addition to fit and battery life, I considered factors such as style; ease of use; the charging case; the strength of the Bluetooth connection; support for assistants such as Siri and Alexa; water resistance ratings; and audio features such as noise cancelation and ambient sound modes. You'll notice I don't have much if anything to say about sound quality. Engadget's resident expert Billy Steele has written plenty about the listening experience in his standalone reviews, which I've linked throughout, but for my purposes the differences were too subtle to make or break a purchasing decision. In the end, I never quite mastered some of the over-complicated controls, but at no point did an earbud fall out while I was exercising. I also never came close to running out of juice.

A spokesperson told the BBC: "We've always known that Muzmatch has unfairly benefitted from our reputation and investment in our brands, and was unrightfully riding Match Group's coat-tails for its own gain.

Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems for providing helpfulness of reviews are being developed. This paper argues that cultural background is an important feature that impacts the nature of a review written by the user, and must be considered as a feature in assessing the helpfulness of online reviews. The paper provides an in-depth study of differences in online reviews written by users from different cultural backgrounds and how incorporating culture as a feature can lead to better review helpfulness recommendations. In particular, we analyze online reviews originating from two distinct cultural spheres, namely Arabic and Western cultures, for two different products, hotels and books. Our analysis demonstrates that the nature of reviews written by users differs based on their cultural backgrounds and that this difference varies based on the specific product being reviewed. Finally, we have developed six different review helpfulness recommendation models that demonstrate that taking culture into account leads to better recommendations.

A wave of new task-based virtual assistants has been fueled by increasingly powerful large language models, such as GPT-4. These conversational agents can be customized to serve customer-specific use cases, but ensuring that agent-generated text conforms to designer-specified rules included in prompt instructions alone is challenging. Therefore, chatbot designers often use another model, called a guardrail model, to verify that the agent output aligns with their rules and constraints. We explore using a distillation approach to guardrail models to monitor the output of the first model using training data from GPT-4. We find two crucial steps to our CONSCENDI process: scenario-augmented generation and contrastive training examples. When generating conversational data, we generate a set of rule-breaking scenarios, which enumerate a diverse set of high-level ways a rule can be violated. This scenario-guided approach produces a diverse training set of rule-violating conversations, and it provides chatbot designers greater control over the classification process. We also prompt GPT-4 to also generate contrastive examples by altering conversations with violations into acceptable conversations. This set of borderline, contrastive examples enables the distilled model to learn finer-grained distinctions between what is acceptable and what is not. We find that CONSCENDI results in guardrail models that improve over baselines.

Tile, best known for its AirTag-like trackers that help you locate lost objects, can now find something that can get lost on purpose -- your cat. The $40 Tile for Cats tracker from Life360 is a modified version of the Tile Sticker with a silicon collar attachment and 250 foot Bluetooth range. The idea is to give you peace of mind that your cat is somewhere in the house, and then help you figure out exactly where that sneaky floof is hiding. The battery on the Tile for Cats lasts a generous three years, and you can easily replace the sticker. It even offers AI assistant integration with Siri, Alexa, and Google Assistant, so you can locate Sir Fluffybutt with a voice command.

Angie Wisdom and Dr. Chirag Shah discuss how artificial intelligence could play a role in online and professional relationships. Artificial intelligence (AI) is already providing significant benefit to people searching for love, negotiating a business deal and struggling with depression. However, according to experts, an over reliance on the technology could cause a "devaluation" of human connection and lead to diminishing authenticity. Rijul Gupta, CEO and co-founder of DeepMedia, told Fox News Digital that as AI continues to evolve, the technology has the potential to enrich social interactions, deliver companionship, and provide support in areas like child-rearing and elder care. Furthermore, Gupta predicted AI is set to "revolutionize" online dating by enhancing existing platforms, such as Tinder and Hinge, with new algorithms that provide more "personalized" and "efficient" matchmaking.

Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN. However, the explainability of these models has not been fully explored. Adding explainability to recommendation models can not only increase trust in the decisionmaking process, but also have multiple benefits such as providing persuasive explanations for item recommendations, creating explicit profiles for users and items, and assisting item producers in design improvements. In this paper, we propose a neat and effective Explainable Collaborative Filtering (ECF) model that leverages interpretable cluster learning to achieve the two most demanding objectives: (1) Precise - the model should not compromise accuracy in the pursuit of explainability; and (2) Self-explainable - the model's explanations should truly reflect its decision-making process, not generated from post-hoc methods. The core of ECF is mining taste clusters from user-item interactions and item profiles.We map each user and item to a sparse set of taste clusters, and taste clusters are distinguished by a few representative tags. The user-item preference, users/items' cluster affiliations, and the generation of taste clusters are jointly optimized in an end-to-end manner. Additionally, we introduce a forest mechanism to ensure the model's accuracy, explainability, and diversity. To comprehensively evaluate the explainability quality of taste clusters, we design several quantitative metrics, including in-cluster item coverage, tag utilization, silhouette, and informativeness. Our model's effectiveness is demonstrated through extensive experiments on three real-world datasets.

Recommender systems predict what items a user will interact with next, based on their past interactions. The problem is often approached through supervised learning, but recent advancements have shifted towards policy optimization of rewards (e.g., user engagement). One challenge with the latter is policy mismatch: we are only able to train a new policy given data collected from a previously-deployed policy. The conventional way to address this problem is through importance sampling correction, but this comes with practical limitations. We suggest an alternative approach of local policy improvement without off-policy correction. Our method computes and optimizes a lower bound of expected reward of the target policy, which is easy to estimate from data and does not involve density ratios (such as those appearing in importance sampling correction). This local policy improvement paradigm is ideal for recommender systems, as previous policies are typically of decent quality and policies are updated frequently. We provide empirical evidence and practical recipes for applying our technique in a sequential recommendation setting.