Top-k recommendation seeks to deliver a personalized list of k items to each individual user. An established methodology in the literature based on matrix factorization (MF), which usually represents users and items as vectors in low-dimensional space, is an effective approach to recommender systems, thanks to its superior performance in terms of recommendation quality and scalability. A typical matrix factorization recommender system has two main phases: preference elicitation and recommendation retrieval. The former analyzes user-generated data to learn user preferences and item characteristics in the form of latent feature vectors, whereas the latter ranks the candidate items based on the learnt vectors and returns the top-k items from the ranked list. For preference elicitation, there have been numerous works to build accurate MF-based recommendation algorithms that can learn from large datasets. However, for the recommendation retrieval phase, naively scanning a large number of items to identify the few most relevant ones may inhibit truly real-time applications. In this work, we survey recent advances and state-of-the-art approaches in the literature that enable fast and accurate retrieval for MF-based personalized recommendations. Also, we include analytical discussions of approaches along different dimensions to provide the readers with a more comprehensive understanding of the surveyed works.

Have you ever wondered how apps like Netflix or Spotify decide which movie or songs you're likely to prefer watching or listening to? Seems like magic, doesn't it? For instance, a lot of data is being mined and multiple complicated algorithms are developed by data science professionals in an attempt to make predictions more accurate. It is not magic but "machine learning." Machine learning is what allows the system to determine the movies and songs most relevant to your liking.

However, recommender At present, most research on the fairness of recommender systems systems can also bring unfavorable consequences, such is conducted either from the perspective of customers or from the as they may narrow the customers' vision [1], or superior items perspective of product(or service) providers. However, such a practice will receive increased attention so as to become dominant [27], ignores the fact that when fairness is guaranteed to one side, while inferior items will be relegated to a lower position, which the fairness and rights of the other side are likely to reduce. In becomes an extremely vicious circle. As a possible unfavorable consequence, this paper, we consider recommendation scenarios from the perspective the unfairness in recommender systems in different aspects, of two sides(customers and providers). From the perspective such as racial/gender stereotypes [22], social polarization of providers, we consider the fairness of the providers' exposure [12], position bias [27], has been a well-studied research topic. in recommender system. For customers, we consider the fairness Problem Statement. Despite the different mechanisms which of the reduced quality of recommendation results due to the have been implemented to ensure the fairness of recommendations, introduction of fairness measures. We theoretically analyzed the these studies only consider the utility of one type of stakeholder relationship between recommendation quality, customers fairness, in business and try to eliminate unfairness among their members.

Feed recommendation models are widely adopted by numerous feed platforms to encourage users to explore the contents they are interested in. However, most of the current research simply focus on targeting user's preference and lack in-depth study of avoiding objectionable contents to be frequently recommended, which is a common reason that let user detest. To address this issue, we propose a Deep Latent Emotion Network (DLEN) model to extract latent probability of a user preferring a feed by modeling multiple targets with semi-supervised learning. With this method, the conflicts of different targets are successfully reduced in the training phase, which improves the training accuracy of each target effectively. Besides, by adding this latent state of user emotion to multi-target fusion, the model is capable of decreasing the probability to recommend objectionable contents to improve user retention and stay time during online testing phase. DLEN is deployed on a real-world multi-task feed recommendation scenario of Tencent QQ-Small-World with a dataset containing over a billion samples, and it exhibits a significant performance advantage over the SOTA MTL model in offline evaluation, together with a considerable increase by 3.02% in view-count and 2.63% in user stay-time in production. Complementary offline experiments of DLEN model on a public dataset also repeat improvements in various scenarios. At present, DLEN model has been successfully deployed in Tencent's feed recommendation system.

While we continue to wait for news about the Mars copter's first test flight, Elon Musk and SpaceX closed out the week with a big win, scoring a contract from NASA to use Starship as a lander for the Artemis lunar program. The company beat out Blue Origin (which teamed up with key aerospace players like Lockheed Martin) and defense contractor Dynetics to secure the $2.9 billion contract. There are still funding hurdles for NASA to clear if it plans to fly as scheduled, but those missions are still years away at best. In the nearer future, Apple's Spring Loaded event is scheduled to take place on Tuesday and Chris Velazco has reminders of the rumors you should know about before it starts. New iPads and iMacs seem like safe bets, but we'll see if there are any big surprises in a few days.

Code-switching is the communication phenomenon where speakers switch between different languages during a conversation. With the widespread adoption of conversational agents and chat platforms, code-switching has become an integral part of written conversations in many multi-lingual communities worldwide. This makes it essential to develop techniques for summarizing and understanding these conversations. Towards this objective, we introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset - GupShup, which contains over 6,831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English. We present a detailed account of the entire data collection and annotation processes. We analyze the dataset using various code-switching statistics. We train state-of-the-art abstractive summarization models and report their performances using both automated metrics and human evaluation. Our results show that multi-lingual mBART and multi-view seq2seq models obtain the best performances on the new dataset

This week brought a bunch of deals on new gadgets, including Amazon's rotating Echo Show 10 and Google's Nest Hub. The former dropped to a new all-time low while the latter remains 20 percent off at various retailers. AirPods Pro are more than $50 off right now, and Amazon Prime members can snag the Fire TV Stick Lite for only $20. Here are the best tech deals from this week that you can still get today. The Nest Audio smart speaker is still $20 off across the web, bringing to down to $80.

When Amazon first introduced Alexa and the Echo speaker six years ago, the idea of talking to a digital assistant wasn't totally novel. Both the iPhone and Android phones had semi-intelligent voice controls -- but with the Echo, Amazon took its first step toward making something like Alexa a constant presence in your home. Since then, Apple and Google have followed suit, and now there's a huge variety of smart speakers available at various price points. As the market exploded, the downsides of having a device that's always listening for a wake word have become increasingly apparent. They can get activated unintentionally, sending private recordings back to monolithic companies to analyze. And even at the best of times, giving more personal information to Amazon, Apple and Google can be a questionable decision. That said, all these companies have made it easier to manage how your data is used -- you can opt out of humans reviewing some of your voice queries, and it's also less complicated to manage and erase your history with various digital assistants, too. The good news is that there's never been a better time to get a smart speaker, particularly if you're a music fan.

Voice control, using either Alexa or Google Assistant, is the U by Moen smart faucet's star attraction, but after testing this kitchen tool for several months, I've concluded that its gesture control feature is far more useful. Voice control is no gimmick, as you'll see when I dig all the things you can do with voice commands. But the tasks for which I use a faucet most often--washing my hands, rinsing dishes, filling a watering can for my houseplants, and the like--waving my hand over the faucet to start the flow of water, and again to stop it is all the technology I need. I love my handmade farmhouse sink, but it seriously complicates changing out the faucet. But that could be because I live in a rural area and draw my water from a well.

If you have ever used Google Maps on your phone without fiddling with the location settings, it goes without saying that the tech giant knows everywhere you've been. The really bad news is that even if you have previously tried to stop Google tracking your every movement, the company may have done so anyway. On Friday the Australian Competition and Consumer Commission (ACCC) won a legal action in the federal court, which ruled that, thanks to a peculiar set-up that required a user to check "No" or "Do Not Collect" to both "Location History" and "Web & App Activity" on some Android and Pixel phones, someone who ticked "No" to just one would still end up being tracked. We asked Dr Katharine Kemp, a legal academic from the University of New South Wales whose focus is consumer law, and the Australian cryptographer Vanessa Teague for their thoughts on the significance of the decision and how a person might go about securing their devices. Kemp, an Apple user herself, says that for many consumers, today's decision may not actually mean much, as the decision only related to Android users and Google has since updated the settings that formed the basis of the ACCC's complaint.