In an era of information explosion, recommendation systems play an important role in people's daily life by facilitating content exploration. It is known that user activeness, i.e., number of behaviors, tends to follow a long-tail distribution, where the majority of users are with low activeness. In practice, we observe that tail users suffer from significantly lower-quality recommendation than the head users after joint training. We further identify that a model trained on tail users separately still achieve inferior results due to limited data. Though long-tail distributions are ubiquitous in recommendation systems, improving the recommendation performance on the tail users still remains challenge in both research and industry. Directly applying related methods on long-tail distribution might be at risk of hurting the experience of head users, which is less affordable since a small portion of head users with high activeness contribute a considerate portion of platform revenue. In this paper, we propose a novel approach that significantly improves the recommendation performance of the tail users while achieving at least comparable performance for the head users over the base model. The essence of this approach is a novel Gradient Aggregation technique that learns common knowledge shared by all users into a backbone model, followed by separate plugin prediction networks for the head users and the tail users personalization. As for common knowledge learning, we leverage the backward adjustment from the causality theory for deconfounding the gradient estimation and thus shielding off the backbone training from the confounder, i.e., user activeness. We conduct extensive experiments on two public recommendation benchmark datasets and a large-scale industrial datasets collected from the Alipay platform. Empirical studies validate the rationality and effectiveness of our approach.

Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose ISCON, which implicitly contextualizes sessions. ISCON first generates implicit contexts for sessions by creating a session-item graph, learning graph embeddings, and clustering to assign sessions to contexts. ISCON then trains a session context predictor and uses the predicted contexts' embeddings to enhance the next-item prediction accuracy. Experiments on four datasets show that ISCON has superior next-item prediction accuracy than state-of-the-art models. A case study of ISCON on the Reddit dataset confirms that assigned session contexts are unique and meaningful.

Late last week, Google research scientist Fei Xia sat in the center of a bright, open-plan kitchen and typed a command into a laptop connected to a one-armed, wheeled robot resembling a large floor lamp. The robot promptly zoomed over to a nearby countertop, gingerly picked up a bag of multigrain chips with a large plastic pincer, and wheeled over to Xia to offer up a snack. The most impressive thing about that demonstration, held in Google's robotics lab in Mountain View, California, was that no human coder had programmed the robot to understand what to do in response to Xia's command. Its control software had learned how to translate a spoken phrase into a sequence of physical actions using millions of pages of text scraped from the web. That means a person doesn't have to use specific preapproved wording to issue commands, as can be necessary with virtual assistants such as Alexa or Siri.

The idea that machines can replicate or even exceed human thinking has served as the inspiration for advanced computing frameworks – and is now seeing vast investment by countless companies. At the center of this concept are artificial intelligence (AI) and machine learning (ML). These terms are often used synonymously and interchangeably. In reality, AI and ML represent two different things--though they are related. Artificial intelligence can be defined as a computing system's ability to imitate or mimic human thinking and behavior. Machine learning, a subset of AI, refers to a system that learns without being explicitly programmed or directly managed by humans.

MOUNTAIN VIEW, Calif., Aug 16 (Reuters) - Alphabet Inc's (GOOGL.O) Google is combining the eyes and arms of physical robots with the knowledge and conversation skills of virtual chatbots to help its employees fetch soda and chips from breakrooms with ease. The mechanical waiters, shown in action to reporters last week, embody an artificial intelligence breakthrough that paves the way for multipurpose robots as easy to control as ones that perform single, structured tasks such as vacuuming or standing guard. Google robots are not ready for sale. They perform only a few dozen simple actions, and the company has not yet embedded them with the "OK, Google" summoning feature familiar to consumers. While Google says it is pursuing development responsibly, adoption could ultimately stall over concerns such as robots becoming surveillance machines, or being equipped with chat technology that can give offensive responses, as Meta Platforms Inc (META.O) and others have experienced in recent years.

In a short period of about 5–10 years, Humanity has met AI. And over this period, some Humans have grown skeptical, pessimistic, and jealous towards AI. And, they have a reason to be. Create enough hunger and we're just monkeys. I look at AI in a very optimistic fashion.

Late last week, Google research scientist Fei Xia sat in the center of a bright, open-plan kitchen and typed a command into a laptop connected to a one-armed, wheeled robot resembling a large floor lamp. The robot promptly zoomed over to a nearby countertop, gingerly picked up a bag of multigrain chips with a large plastic pincer, and wheeled over to Xia to offer up a snack. The most impressive thing about that demonstration, held in Google's robotics lab in Mountain View, California, was that no human coder had programmed the robot to understand what to do in response to Xia's command. Its control software had learned how to translate a spoken phrase into a sequence of physical actions using millions of pages of text scraped from the web. That means a person doesn't have to use specific preapproved wording to issue commands, as can be necessary with virtual assistants such as Alexa or Siri.

As recommender systems become increasingly sophisticated and complex, they often suffer from lack of fairness and transparency. Providing robust and unbiased explanations for recommendations has been drawing more and more attention as it can help address these issues and improve trustworthiness and informativeness of recommender systems. However, despite the fact that such explanations are generated for humans who respond more strongly to messages with appropriate emotions, there is a lack of consideration for emotions when generating explanations for recommendations. Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning. In this paper, we propose a novel method based on a multi-head transformer, called Emotion-aware Transformer for Explainable Recommendation (EmoTER), to generate more robust, fair, and emotion-enhanced explanations. To measure the linguistic quality and emotion fairness of the generated explanations, we adopt both automatic text metrics and human perceptions for evaluation. Experiments on three widely-used benchmark datasets with multiple evaluation metrics demonstrate that EmoTER consistently outperforms the existing state-of-the-art explanation generation models in terms of text quality, explainability, and consideration for fairness to emotion distribution. Implementation of EmoTER will be released as an open-source toolkit to support further research.

Prediction models can exhibit sensitivity with respect to training data: small changes in the training data can produce models that assign conflicting predictions to individual data points during test time. In this work, we study this sensitivity in recommender systems, where users' recommendations are drastically altered by minor perturbations in other unrelated users' interactions. We introduce a measure of stability for recommender systems, called Rank List Sensitivity (RLS), which measures how rank lists generated by a given recommender system at test time change as a result of a perturbation in the training data. We develop a method, CASPER, which uses cascading effect to identify the minimal and systematical perturbation to induce higher instability in a recommender system. Experiments on four datasets show that recommender models are overly sensitive to minor perturbations introduced randomly or via CASPER - even perturbing one random interaction of one user drastically changes the recommendation lists of all users. Importantly, with CASPER perturbation, the models generate more unstable recommendations for low-accuracy users (i.e., those who receive low-quality recommendations) than high-accuracy ones.

If you've been in the market for a large smart display, it might be worth taking a look at Amazon's Echo Show 15, which is currently on sale. You can snap one up for $190, which is $60 off the regular price of $250. Amazon released the device last year and we gave it a score of 78 in our review. We admired the large, bright screen and the picture frame design. We found the widgets (which include ones for headlines, weather, calendar, sticky notes, recipe suggestions and package delivery tracking) to be handy.