A sequential recommender system aims to recommend attractive items to users based on behaviour patterns. The predominant sequential recommendation models are based on natural language processing models, such as the gated recurrent unit, that embed items in some defined space and grasp the user's long-term and short-term preferences based on the item embeddings. However, these approaches lack fundamental insight into how such models are related to the user's inherent decision-making process. To provide this insight, we propose a novel recurrent cell, namely FaNC, from Freudian and Newtonian perspectives. FaNC divides the user's state into conscious and unconscious states, and the user's decision process is modelled by Freud's two principles: the pleasure principle and reality principle. To model the pleasure principle, i.e., free-floating user's instinct, we place the user's unconscious state and item embeddings in the same latent space and subject them to Newton's law of gravitation. Moreover, to recommend items to users, we model the reality principle, i.e., balancing the conscious and unconscious states, via a gating function. Based on extensive experiments on various benchmark datasets, this paper provides insight into the characteristics of the proposed model. FaNC initiates a new direction of sequential recommendations at the convergence of psychoanalysis and recommender systems.

Ever heard a catchy song on your smart speaker and wanted to share it right away? You can act on that impulse if you have an Echo. Amazon is rolling out a music sharing feature that lets you share songs with Alexa contacts using Echo devices or the Alexa app. Ask the voice assistant to "share this song with" a contact and they can not only choose to listen, but send a reaction. Thankfully, you don't need to use Amazon Music or even the same streaming service as your recipient.

Recommender systems are important and valuable tools for many personalized services. Collaborative Filtering (CF) algorithms -- among others -- are fundamental algorithms driving the underlying mechanism of personalized recommendation. Many of the traditional CF algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data for matching, including memory-based methods such as user/item-based CF as well as learning-based methods such as matrix factorization and deep learning models. However, advancing from correlative learning to causal learning is an important problem, because causal/counterfactual modeling can help us to think outside of the observational data for user modeling and personalization. In this paper, we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommendation. We first provide a unified causal view of CF and mathematically show that many of the traditional CF algorithms are actually special cases of CCF under simplified causal graphs. We then propose a conditional intervention approach for $do$-calculus so that we can estimate the causal relations based on observational data. Finally, we further propose a general counterfactual constrained learning framework for estimating the user-item preferences. Experiments are conducted on two types of real-world datasets -- traditional and randomized trial data -- and results show that our framework can improve the recommendation performance of many CF algorithms.

Online-dating company Match Group Inc. has reached an agreement to acquire South Korean social-media company Hyperconnect for $1.73 billion, broadening its services beyond connecting people in their love lives. The cash-and-stock deal, announced Tuesday, marks Match Group's largest acquisition to date. Hyperconnect, based in Seoul, has developed two video apps that focus on helping people interact one-on-one and with new communities. Hyperconnect's first app, Azar, offers live video and audio chat and can instantly translate voice and text for users that speak different languages. Hyperconnect's other app, Hakuna Live, is a social live-streaming app that provides group video and audio broadcasts.

Instagram is taking on TikTok with a new update that will de-rank Reels with its competitor's watermark. The update is being unleashed to an algorithm tasked with recommending Reels with the hopes of users creating unique content on the platform – and not just recycling from other apps. Instagram launched Reels just five days before former President Donald Trump announced plans to ban Chinese-owned TikTok in the US, with the hopes of being the front runner in the video content space. However, six months later and Reels has not gained uptick in usage as Instagram had hoped and TikTok is still around and seems to be thriving with its some 100 million users in just the US alone – which may be why Instagram is not promoting the watermarked clips. Reels offers users shot-form video editing tools that lets users be creative with 30-second video clips with captions and music.

It is 2020 and it's all about technology these days. You knowingly or unknowingly use machine learning in your day-to-day life. Let me give you some examples, Google spam filter, Netflix recommendation system, Facebook face recognition, weather forecast, and much more. All of these are Machine Learning. It has become one of the hottest industries and has become like a trend for beginners.

Valentine's Day is just around the corner -- and if you want to maintain the interest of a woman on Tinder, a funny chat-up line is the way to go, scientists have revealed. US researchers tested out various online chat-up lines on 237 young, heterosexual adults -- finding that humour was a better opening gambit than compliments. In fact, they found that men who used funny introductions were seen as more attractive to women, who rated them as more intelligent, kind and trustworthy. Even if some of the lines were a little cheesy, they found that women still responded to them better than bland, unimaginative greetings like'Hi, how are you?' Men, in contrast, were found to overwhelmingly base their evaluations of prospective dates on how attractive they found the woman's profile. The team noted that, thanks to the COVID-19 pandemic closing bars and clubs around the world, singles have surged to apps like Tinder for their dating needs.

Research has shown that trust is an essential aspect of human-computer interaction directly determining the degree to which the person is willing to use a system. An automatic prediction of the level of trust that a user has on a certain system could be used to attempt to correct potential distrust by having the system take relevant actions like, for example, apologizing or explaining its decisions. In this work, we explore the feasibility of automatically detecting the level of trust that a user has on a virtual assistant (VA) based on their speech. We developed a novel protocol for collecting speech data from subjects induced to have different degrees of trust in the skills of a VA. The protocol consists of an interactive session where the subject is asked to respond to a series of factual questions with the help of a virtual assistant. In order to induce subjects to either trust or distrust the VA's skills, they are first informed that the VA was previously rated by other users as being either good or bad; subsequently, the VA answers the subjects' questions consistently to its alleged abilities. All interactions are speech-based, with subjects and VAs communicating verbally, which allows the recording of speech produced under different trust conditions. Using this protocol, we collected a speech corpus in Argentine Spanish. We show clear evidence that the protocol effectively succeeded in influencing subjects into the desired mental state of either trusting or distrusting the agent's skills, and present results of a perceptual study of the degree of trust performed by expert listeners. Finally, we found that the subject's speech can be used to detect which type of VA they were using, which could be considered a proxy for the user's trust toward the VA's abilities, with an accuracy up to 76%, compared to a random baseline of 50%.

There is a growing desire to create computer systems that can communicate effectively to collaborate with humans on complex, open-ended activities. Assessing these systems presents significant challenges. We describe a framework for evaluating systems engaged in open-ended complex scenarios where evaluators do not have the luxury of comparing performance to a single right answer. This framework has been used to evaluate human-machine creative collaborations across story and music generation, interactive block building, and exploration of molecular mechanisms in cancer. These activities are fundamentally different from the more constrained tasks performed by most contemporary personal assistants as they are generally open-ended, with no single correct solution, and often no obvious completion criteria. We identified the Key Properties that must be exhibited by successful systems. From there we identified "Hallmarks" of success -- capabilities and features that evaluators can observe that would be indicative of progress toward achieving a Key Property. In addition to being a framework for assessment, the Key Properties and Hallmarks are intended to serve as goals in guiding research direction.

Amazon announced that their voice assistant Alexa can now sign business agreements with health providers under the Health Insurance Portability and Accountability Act, or HIPAA. Third-party health developers can meet the rules that govern how sensitive health information is shared, and major health providers and companies have launched a number of voice programs to help users manage chronic conditions. If you have an Amazon Echo at home, you need to dive into the privacy settings. There are a few important things to lockdown. Don't forget to turn off voice purchasing if you never use that feature, or at least set up a PIN.