This paper considers the problem of updating the rank-k truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time. Such matrix problems represent an important computational kernel in applications such as Latent Semantic Indexing and Recommender Systems. Nonetheless, the proposed framework is purely algebraic and targets general updating problems. The algorithm presented in this paper undertakes a projection view-point and focuses on building a pair of subspaces which approximate the linear span of the sought singular vectors of the updated matrix. We discuss and analyze two different choices to form the projection subspaces. Results on matrices from real applications suggest that the proposed algorithm can lead to higher accuracy, especially for the singular triplets associated with the largest modulus singular values. Several practical details and key differences with other approaches are also discussed.

Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around items. State-of-the-art TCF methods employ recurrent neural networks (RNNs) to model such aspects. These methods deploy matrix-factorization-based (MF-based) approaches to learn the user and item representations. Recently, graph-neural-network-based (GNN-based) approaches have shown improved performance in providing accurate recommendations over traditional MF-based approaches in non-temporal CF settings. Motivated by this, we propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics. A challenge with this method lies in the increased data sparsity, which negatively impacts obtaining meaningful quality representations with GNNs. To overcome this challenge, we train a GNN model at each time step using a set of observed interactions accumulated time-wise. Comprehensive experiments on real-world data show the improved performance obtained by our method over several state-of-the-art temporal and non-temporal CF models.

With the growing utility of today's conversational virtual assistants, the importance of user motivation in human-AI interaction is becoming more obvious. However, previous studies in this and related fields, such as human-computer interaction and human-robot interaction, scarcely discussed intrinsic motivation and its affecting factors. Those studies either treated motivation as an inseparable concept or focused on non-intrinsic motivation. The current study aims to cover intrinsic motivation by taking an affective-engineering approach. A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty. Experiments are conducted where these two factors are manipulated by making participants believe they are interacting with the smart speaker "Amazon Echo". Intrinsic motivation is measured both by using questionnaires and by covertly monitoring a five-minute free-choice period in the experimenter's absence, during which the participants could decide for themselves whether to interact with the virtual assistants. Results of the first experiment showed that high expectation engenders more intrinsically motivated interaction compared with low expectation. The results also suggested suppressive effects by uncertainty on intrinsic motivation, though we had not hypothesized before experiments. We then revised our hypothetical model of action selection accordingly and conducted a verification experiment of uncertainty's effects. Results of the verification experiment showed that reducing uncertainty encourages more interactions and causes the motivation behind these interactions to shift from non-intrinsic to intrinsic.

We are 100% focused on customer success, so we only get paid if we get you a sale. Our solution is actually free for you. There is no CPC, or set up fee. We just charge a small fee for every product we help you to sell. This is how it works: we connect a tracking pixel to every product we recommend, at the end of the month, we will send you an invoice for the products that users have bought using RecoMind.

The original Chromecast that debuted in 2013 was a simple $35 dongle. But it was still notable, providing a cheap way to make any TV "smart." Things have changed a lot since then, however. Not only do a lot of TVs now come with built-in apps, Roku and Amazon developed their own streaming sticks over the years -- both of which have remote controls and visual menus for easy navigation. Google's Chromecast soon seemed outdated by comparison.

Amazon is gearing up for its latest hardware event where it's expected to announce its latest smart home gear. Based on prior years, we could see new Echo speakers, Alexa updates, Eero routers, Ring cameras, and more. But looking at Amazon's smart home lineup right now, the question isn't whether Amazon will have new hardware to show off. It's whether Amazon can convince its current customers to buy them. Look at the current slate of Echos or Eeros, for example.

It's been ten years since Nest first launched its first smart thermostat, and it's become the most popular brand in the connected home temperature control space. If you've seen one of its products, you'll recognize the distinctive puck-like shape and rotating edge controls. Now that it's ten years old, though, it's time for the thermostat to get a glow up. Google is launching the new Nest Thermostat today for $130, and it features an impressively sleek, attractive makeover that'll make the device look less like a bump on your wall and more like an elegant ornament. It's available in four colors --snow, sand, charcoal, and fog and looks significantly smaller than before.

Financial institutions such as banks are at the forefront of technological innovations, looking for ways to execute faster and serve their customers better. It may be tempting to embrace whatever technology comes on the way in striving for the latest and greatest solutions. It has led to the proliferation of chatbots that claim to reinforce call centres with automation. In reality, these bots behave like dated robots and are stubborn in how they communicate with customers, building an IVR 2.0 format that frustrates callers and does not allow banks from serving customers. Due to the pandemic and the possibility of subsequent lockdowns, this seems challenging.

If you're waiting for Amazon Prime Day to kick off tomorrow, you may want to take advantage of the deals that other retailers already have going on. Walmart has already kicked off its own "anti-Prime Day" savings event and with it comes the best price we've seen on the Lenovo Smart Clock. Right now, Walmart has the smart alarm clock for $39, which is $1 cheaper than its previous low and 50 percent off its normal price. This little gadget has gotten quite popular since its release last year. We gave it a score of 87 for its charming design, ambient light sensor, sunrise alarm feature and lack of camera.

We're in 2020 and long past the days back when we used to stand outside the school library to get the opportunity to copy two or three Encyclopedia pages, to use as a kind of reference for our school projects. With this age having grown up with the benefit of access to technology at their fingertips, the field of education has hugely changed and overturned in this digitally driven world. Artificial Intelligence in the education market was worth US$2.022 billion for the year 2019. The worldwide AI in the education market is anticipated to be valued at USD 3.68 billion by 2023, at a CAGR of 47% during the forecast period of 2018 till 2023. Artificial intelligence has already infiltrated our lives on an individual level.