Parents want the best for their children. While the goal is apparent, figuring out how to do it is challenging. Parents want their kids to be healthy, happy, secure, smart, sociable, smart, athletic, etc. That is a lot to do considering that they have to balance it out based on how quality time they can make for their child, how much resources they can put in to child development, meeting socio-economic needs, etc. With everyone turning to artificial intelligent assistants for help, could there be an AI digital assistant for parents doing one of the most human of things: raising their children?

Graph-based recommender systems (GRSs) analyze the structural information in the graphical representation of data to make better recommendations, especially when the direct user-item relation data is sparse. Ranking-oriented GRSs that form a major class of recommendation systems, mostly use the graphical representation of preference (or rank) data for measuring node similarities, from which they can infer a recommendation list using a neighborhood-based mechanism. In this paper, we propose PGRec, a novel graph-based ranking-oriented recommendation framework. PGRec models the preferences of the users over items, by a novel graph structure called PrefGraph. This graph is then exploited by an improved embedding approach, taking advantage of both factorization and deep learning methods, to extract vectors representing users, items, and preferences. The resulting embedding are then used for predicting users' unknown pairwise preferences from which the final recommendation lists are inferred. We have evaluated the performance of the proposed method against the state of the art model-based and neighborhood-based recommendation methods, and our experiments show that PGRec outperforms the baseline algorithms up to 3.2% in terms of NDCG@10 in different MovieLens datasets.

Google wants to make its smart displays more fun. Today, it's adding a handful of new games that you can play with Google Assistant on smart displays like the Nest Hub Max. If you're a trivia fan, you might choose to play Jeopardy! There are also wordplay games, a drawing game and Google's version of an escape-the-room challenge. Google Assistant was already capable of playing a few voice-based games.

During Alexa Live 2020, Amazon announced a new Alexa Skills Challenge with conversations. The deadline to submit is at 5pm EDT on September 14, 2020 (47 days away). You can submit and find the full details by visiting https://alexaconversations.devpost.com/. Here are some items to get started with. Good luck if you choose to participate.

Apple has updated its Store app for iOS and iPadOS with a new tab that shows all the devices linked to your Apple ID along with shopping suggestions based on that list. As 9to5Mac notes, tapping on the tab shows an overview of the iPhones, iPads and Macs you have under the "Your devices" list. The new section also shows accessories you can buy that are compatible with your devices. And if you have an iPhone, tapping on it shows its warranty information. In case it doesn't have one anymore, the app will display a trade-in value instead, as well as a quick link to start the Apple Trade In process.

The problem of MPI programs execution time prediction on a certain set of computer installations is considered. This problem emerges with orchestration and provisioning a virtual infrastructure in a cloud computing environment over a heterogeneous network of computer installations: supercomputers or clusters of servers (e.g. mini data centers). One of the key criteria for the effectiveness of the cloud computing environment is the time staying by the program inside the environment. This time consists of the waiting time in the queue and the execution time on the selected physical computer installation, to which the computational resource of the virtual infrastructure is dynamically mapped. One of the components of this problem is the estimation of the MPI programs execution time on a certain set of computer installations. This is necessary to determine a proper choice of order and place for program execution. The article proposes two new approaches to the program execution time prediction problem. The first one is based on computer installations grouping based on the Pearson correlation coefficient. The second one is based on vector representations of computer installations and MPI programs, so-called embeddings. The embedding technique is actively used in recommendation systems, such as for goods (Amazon), for articles (Arxiv.org), for videos (YouTube, Netflix). The article shows how the embeddings technique helps to predict the execution time of a MPI program on a certain set of computer installations.

The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take advantage of all the available contextual match data and dismiss potentially valuable information. To address this problem we develop TTIR, a contextual recommender model derived from the Transformer neural architecture that suggests a set of items to every team member, based on the contexts of teams and roles that describe the match. TTIR outperforms several approaches and provides interpretable recommendations through visualization of attention weights. Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task. Furthermore, a preliminary user survey indicates the usefulness of attention weights for explaining recommendations as well as ideas for future work. The code and dataset are available at: https://github.com/ojedaf/IC-TIR-Lol.

A context-aware recommender system (CARS) applies sensing and analysis of user context to provide personalized services. The contextual information can be driven from sensors in order to improve the accuracy of the recommendations. Yet, generating accurate recommendations is not enough to constitute a useful system from the users' perspective, since certain contextual information may cause different issues, such as draining the user's battery, privacy issues, and more. Adding high-dimensional contextual information may increase both the dimensionality and sparsity of the model. Previous studies suggest reducing the amount of contextual information by selecting the most suitable contextual information using a domain knowledge. Another solution is compressing it into a denser latent space, thus disrupting the ability to explain the recommendation item to the user, and damaging users' trust. In this paper we present an approach for selecting low-dimensional subsets of the contextual information and incorporating them explicitly within CARS. Specifically, we present a novel feature-selection algorithm, based on genetic algorithms (GA), that outperforms SOTA dimensional-reduction CARS algorithms, improves the accuracy and the explainability of the recommendations, and allows for controlling user aspects, such as privacy and battery consumption. Furthermore, we exploit the top subsets that are generated along the evolutionary process, by learning multiple deep context-aware models and applying a stacking technique on them, thus improving the accuracy while remaining at the explicit space. We evaluated our approach on two high-dimensional context-aware datasets driven from smartphones. An empirical analysis of our results validates that our proposed approach outperforms SOTA CARS models while improving transparency and explainability to the user.

In the same way a smart video doorbell keeps a watchful eye over your home, a smart pool water monitor can help keep tabs on the quality of your precious pool water. It may seem like a trivial gadget to add to your home, but if you've spent any time schlepping water from your pool to the pool store every week, you know how time-consuming it can be. And, during a pandemic-riddled summer when social distancing is still in effect, taking matters into your own hands may just be the way to go. I've tried all sorts of smart gadgets before but nothing like the pHin smart pool water monitor. The pHin smart pool water monitor comes neatly packaged and includes everything you need, including a bridge, to set it up.

With Smart Home technology taking off and smart home assistants (like Amazon Alexa, Google Home and Apple Siri) becoming more prevalent in homes the start of a new home technology revolution is underway. Homeowners can monitor and control the house heating and cooling systems, security systems, door locks, garage doors and more all from where ever they have access to internet on their smartphone. Artificial intelligence (AI) will add to that ability by allowing decisions about the home to be made without the need of direct input from the homeowner. For instance a trusted dog walker walks up to the front door during their scheduled time to take Fido out for a walk. The dog walker's face is seen via camera which an artificial intelligence assistant recognizes and knows they are there during the correct time and allows the door to be unlocked so Fido can enjoy some outdoor time while the homeowner is away.