User behavior has been validated to be effective in revealing personalized preferences for commercial recommendations. However, few user-item interactions can be collected for new users, which results in a null space for their interests, i.e., the cold-start dilemma. In this paper, a two-tower framework, namely, the model-agnostic interest learning (MAIL) framework, is proposed to address the cold-start recommendation (CSR) problem for recommender systems. In MAIL, one unique tower is constructed to tackle the CSR from a zero-shot view, and the other tower focuses on the general ranking task. Specifically, the zero-shot tower first performs cross-modal reconstruction with dual auto-encoders to obtain virtual behavior data from highly aligned hidden features for new users; and the ranking tower can then output recommendations for users based on the completed data by the zero-shot tower. Practically, the ranking tower in MAIL is model-agnostic and can be implemented with any embedding-based deep models. Based on the co-training of the two towers, the MAIL presents an end-to-end method for recommender systems that shows an incremental performance improvement. The proposed method has been successfully deployed on the live recommendation system of NetEase Cloud Music to achieve a click-through rate improvement of 13% to 15% for millions of users. Offline experiments on real-world datasets also show its superior performance in CSR. Our code is available.

Amazon might have made your choice of true wireless earbuds a little easier. The internet retailer is running a sale on the second-generation Echo Buds that lowers the price to $90 for the standard version, and $105 for the model with a wireless charging case. That makes them less expensive than many no-frills earbuds, let alone ones with comparable features like active noise cancellation. These aren't the absolute best-sounding earbuds you'll buy, but they pair solid quality with perks difficult to find even at their normal prices, including ANC, IPX4 water resistance and built-in Alexa support. These may be just the ticket if you're interested in hushing the outside world or adding a soundtrack to your workouts.

Artificial intelligence and machine learning are two popular and often hyped terms these days. And people often use them interchangeably to describe an intelligent software or system. But even though both AI and ML are based on statistics and mathematics, they are not the same thing. In this article, you will learn the differences between AI and ML with some practical examples to help clear up any confusion. Artificial intelligence, or AI, is the ability of a computer or machine to mimic or imitate human intelligent behavior and perform human-like tasks.

AI technology is radically changing the outlook for the ecommerce sector in a number of very promising ways.

Our personal devices would know us more than we know ourselves. They might even increase our life span. That Artificial Intelligence (AI) will change the ways of the world in the 2020s is a foregone conclusion. Perhaps its greatest--and most defining--impact would be felt on personal devices and the way humans interact with them. 'Emotion AI' systems are becoming so nuanced and powerful that our devices will soon know more about our emotional being than our friends or family ever did.

As we enter another new year, there are a fresh set of forecasts, hopes, and fears about the path of digital marketing in the coming year and beyond. The recurrence of two themes: Artificial Intelligence (AI) and Machine Learning (ML), is one of the fascinating topics found across numerous trends. Marketing automation, digital creativity, customization, and marketing technology are just a few of the areas where these two closely related topics appear to be gaining traction. When it comes to finding new information, 84 percent of individuals say they look to the internet first. Mobile devices account for 50% of all search inquiries.

An artificial intelligence-powered platform designed to enhance traditional mobile banking with seamless crypto integration is about to launch v2 of its mobile application, describing it as an all-in-one crypto-financial solution. BlockBank, which describes itself as a platform designed for professional traders and new retail market participants, reported that the new version of its application is a significant advancement compared to what has been offered in the space so far. The application is said to consist of four main components: a centralized custodial wallet, a non-custodial Web 3.0 wallet, banking and an AI-powered robo-advisor. The team emphasized that users won't have to sacrifice security, privacy or decentralization when using its application. One of the measures taken to ensure a high level of security is BlockBank's partnership with Shield Finance, a multichain decentralized finance insurance aggregator, and Bridge Mutual, a platform that integrates a DeFi risk coverage application into its system.

Alexa is the natural language processing based system by Amazon. Alexa is the virtual assistant in products like Amazon Echo, Dot, Tap, FireTV and other third party products (there are 100 of these). The technology was first launched in 2012 and has now become an integral part of all of our lives. From kids have found someone who can help with their homework, to elderly who lean on Alexa as a reliable partner for reminding them of their medication to daily chores. For young professionals and busy moms it gives freedom to make lists, control smart homes etc with simply the use of voice commands.

Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset of thousands of items. We propose a Global-Local Kernel-based matrix completion framework, named GLocal-K, that aims to generalise and represent a high-dimensional sparse user-item matrix entry into a low dimensional space with a small number of important features. Our GLocal-K can be divided into two major stages. First, we pre-train an auto encoder with the local kernelised weight matrix, which transforms the data from one space into the feature space by using a 2d-RBF kernel. Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item. We apply our GLocal-K model under the extreme low-resource setting, which includes only a user-item rating matrix, with no side information. Our model outperforms the state-of-the-art baselines on three collaborative filtering benchmarks: ML-100K, ML-1M, and Douban.

In this paper we study the personalized book recommender system in a child-robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children's fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance and it can operate on embedded consumer devices with limited computational resources.