Your iPhone will make it easier to find a location to receive a COVID-19 vaccination. Apple announced Tuesday it will display on its Apple Maps app locations where users can go to get a COVID vaccine. The data comes from VaccineFinder, a free, online service developed by Boston Children's Hospital that offers information on where they can get a vaccine. Apple product users can search for a nearby vaccination site by asking Siri or typing in the search bar within Maps. When you click on a specific site within Maps, you will find details including address, phone number and operating hours.

The newest version of Google's Nest Hub promises better sound – and better sleep. But consumers having trouble sleeping may be more drawn to its new built-in Sleep Sensing technology that uses Google's Soli motion detection chip – first used in the Pixel 4 smartphone – to know how long you slept. The Soli-driven radar does not identify specific bodies or faces, but will watch your movements and breathing, as well as track when you snore or cough to build a pattern of your sleep – and what could be interrupting it. Facebook's vaccine finder:The new tool can help you book a COVID-19 vaccine appointment Google's consumer research found about 20% of Nest Hub displays owned by consumers are in bedrooms. "When we talked to people about what they most wanted help with, they resoundingly responded with'Can you help me get better quality of sleep?' That's what people were really interested in," Ashton Udall, senior Nest product manager, told USA TODAY.

Although virtual assistants have been around for a while, and most consumers are used to dealing with them, the application is limited and the experience can be poor. However, there is a whole new level of virtual assistance on the way called advanced virtual assistants, which are capable of functioning as virtual billing agents, virtual AI/VR agents or even virtual driver/car agents. The impact will be substantial in industries, organizations and consumer interactions. But advanced virtual assistants are just one of 23 of the most impactful technologies featured on the Gartner Emerging Technologies and Trends Impact Radar for 2021. Let's look at some of the technologies that I find especially interesting. I'll examine a few that are right around the corner (e.g., advanced virtual assistants) and one that is further out (AR cloud).

Personal health libraries (PHLs) provide a single point of secure access to patients digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients health by understanding medical events in the context of their lives. This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults. We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations. The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge.

Social recommendation is effective in improving the recommendation performance by leveraging social relations from online social networking platforms. Social relations among users provide friends' information for modeling users' interest in candidate items and help items expose to potential consumers (i.e., item attraction). However, there are two issues haven't been well-studied: Firstly, for the user interests, existing methods typically aggregate friends' information contextualized on the candidate item only, and this shallow context-aware aggregation makes them suffer from the limited friends' information. Secondly, for the item attraction, if the item's past consumers are the friends of or have a similar consumption habit to the targeted user, the item may be more attractive to the targeted user, but most existing methods neglect the relation enhanced context-aware item attraction. To address the above issues, we proposed DICER (Dual Side Deep Context-aware Modulation for SocialRecommendation). Specifically, we first proposed a novel graph neural network to model the social relation and collaborative relation, and on top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction. Empirical results on two real-world datasets show the effectiveness of the proposed model and further experiments are conducted to help understand how the dual context-aware modulation works.

Recently, deep neural networks are widely applied in recommender systems for their effectiveness in capturing/modeling users' preferences. Especially, the attention mechanism in deep learning enables recommender systems to incorporate various features in an adaptive way. Specifically, as for the next item recommendation task, we have the following three observations: 1) users' sequential behavior records aggregate at time positions ("time-aggregation"), 2) users have personalized taste that is related to the "time-aggregation" phenomenon ("personalized time-aggregation"), and 3) users' short-term interests play an important role in the next item prediction/recommendation. In this paper, we propose a new Time-aware Long- and Short-term Attention Network (TLSAN) to address those observations mentioned above. Specifically, TLSAN consists of two main components. Firstly, TLSAN models "personalized time-aggregation" and learn user-specific temporal taste via trainable personalized time position embeddings with category-aware correlations in long-term behaviors. Secondly, long- and short-term feature-wise attention layers are proposed to effectively capture users' long- and short-term preferences for accurate recommendation. Especially, the attention mechanism enables TLSAN to utilize users' preferences in an adaptive way, and its usage in long- and short-term layers enhances TLSAN's ability of dealing with sparse interaction data. Extensive experiments are conducted on Amazon datasets from different fields (also with different size), and the results show that TLSAN outperforms state-of-the-art baselines in both capturing users' preferences and performing time-sensitive next-item recommendation.

The news recommender systems are marked by a few unique challenges specific to the news domain. These challenges emerge from rapidly evolving readers' interests over dynamically generated news items that continuously change over time. News reading is also driven by a blend of a reader's long-term and short-term interests. In addition, diversity is required in a news recommender system, not only to keep the reader engaged in the reading process but to get them exposed to different views and opinions. In this paper, we propose a deep neural network that jointly learns informative news and readers' interests into a unified framework. We learn the news representation (features) from the headlines, snippets (body) and taxonomy (category, subcategory) of news. We learn a reader's long-term interests from the reader's click history, short-term interests from the recent clicks via LSTMSs and the diversified reader's interests through the attention mechanism. We also apply different levels of attention to our model. We conduct extensive experiments on two news datasets to demonstrate the effectiveness of our approach.

Tinder has skyrocketed in popularity amid the coronavirus pandemic, allowing users who are shut in their homes to still have a chance to meet a romantic partner. However, the app has also come under fire for its user base – it has received numerous reports of abuse. A US report from last year found women under 35 using Tinder were twice as likely as their male counterparts to be called offensive names, or physically threatened, by someone they met on the dating app. Tracey Breeden, Head of Safety and Social Advocacy for Match Group, said: 'For far too long women and marginalized groups in all corners of the world have faced many barriers to resources and safety.'

The owner of massive dating apps Tinder and Match has just announced a new partnership to help keep its users safe. Match Group, which owns Tinder, Match, OK Cupid, Hinge and several other services, has made an investment in Garbo, a non-profit, female-founded background check platform. As part of the deal, Garbo's platform will be available to people using Match Group apps, starting with Tinder later this year. If you're not familiar with Garbo, it was founded by Kathryn Kosmides, a "survivor of gender-based violence" who wanted to make it easier to find information about people you may connect with online. Garbo's platform aggregates numerous data sources to provide details on an individual, including "arrests, convictions, restraining orders, harassment, and other violent crimes."

Most conversational recommendation approaches are either not explainable, or they require external user's knowledge for explaining or their explanations cannot be applied in real time due to computational limitations. In this work, we present a real time category based conversational recommendation approach, which can provide concise explanations without prior user knowledge being required. We first perform an explainable user model in the form of preferences over the items' categories, and then use the category preferences to recommend items. The user model is performed by applying a BERT-based neural architecture on the conversation. Then, we translate the user model into item recommendation scores using a Feed Forward Network. User preferences during the conversation in our approach are represented by category vectors which are directly interpretable. The experimental results on the real conversational recommendation dataset ReDial [12] demonstrate comparable performance to the state-of-the-art, while our approach is explainable. We also show the potential power of our framework by involving an oracle setting of category preference prediction. Keywords: Conversational Recommendation · Category Preference Based Recommendation · Explainable Conversational Recommendation · Cold Start Explainable Recommendation.