In the task of factoid question answering over knowledge base, many questions have more than one plausible interpretation. Previous works on SimpleQuestions assume only one interpretation as the ground truth for each question, so they lack the ability to answer ambiguous questions correctly. In this paper, we present a new way to utilize the dataset that takes into account the existence of ambiguous questions. Then we introduce a simple and effective model which combines local knowledge subgraph with attention mechanism. Our experimental results show that our approach achieves outstanding performance in this task.

We generalize to the finite-state case the notion of the extreme effect variable $Y$ that accumulates all the effect of a variant variable $V$ observed in changes of another variable $X$. We conduct theoretical analysis and turn the problem of finding of an effect variable into a problem of a simultaneous decomposition of a set of distributions. The states of the extreme effect variable, on the one hand, are minimally affected by the variant variable $V$ and, on the other hand, are extremely different with respect to the observable variable $X$. We apply our technique to online evaluation of a web search engine through A/B testing and show its utility.

As brands are rapidly implementing new technologies to improve customer experiences, most customer believe that the future should entail of human and automated support. The future should comprise of automation, AI and humans working together to deliver emotionally intelligent customer experiences.

From simple website optimization for the desktop to the complex and ever-evolving process of enhancing the content, Search Engine Optimization has gone through various changes over the last decade. While some aspects have never changed, such as keywords and meta tags optimizations and link building, SEO evolved a little more when it came to mobile optimization, user experience, and social media marketing. But a big change as notices when Google introduced RankBrain in 2016. RankBrain used a machine-learning algorithm to identify the patterns and the bucket data, and the process resulted in revealing a new system that analyzes a new Google search. Since then, SEO has been changing a lot with Artificial Intelligence and Machine Learning algorithms making tremendous betterment in improving the relevance of content for the searcher. And in the coming 2020, AI is very likely to impact the future of SEO through videos, images, voice search, and pre-trained models. Let's hear a little more about it from the experts – With the inevitable advancement of AI, it has become a necessity for every marketer to reform their marketing strategy to include AI. On top of that, AI tools will also help improve keyword research methods for better content strategy, stronger analytics and reporting system for SEO teams, and smarter personalization. Early adopters will surely reap the benefits of AI. Next, we have Dawn Anderso, Managing Director at Bertey – International SEO & digital strategy consultant, speaker, trainer, and lecturer. The impact of AI on SEO in 2020 is not anywhere near as much as you think.

Intelligent search: As advanced technology solutions grow smarter, it's important to remember that audiences are becoming smarter as well. Thanks to social media and rapid-fire search engines (like Google!), people find what they are looking for faster than ever before. AI and big data solutions can actually analyse these search patterns and help marketers identify key areas where they should focus their efforts. Smart ads: Marketers like Tata Capital are already experimenting with smarter ads, with account-based marketing solutions, but AI helps teams take this a layer further for truly insightful analysis. AI solutions are dig deep into keyword searches, social profiles, and other online data for human-level outcomes.

Facebook dominated the year in terms of app downloads, with 4 out of 5 of the most downloaded apps belonging to the same company. App Annie's year-end report shows that worldwide app downloads hit a record high of 120 billion across iOS and Android in 2019, which represents a 5% year-over-year increase. Related: 6 Things Marketers Should Know About Facebook's App Integrations It should be noted that App Annie's data does not included include re-installs or app updates, only net new downloads are counted. Consumer spending on apps is growing as well and will approach $90 billion worldwide in 2019. App Annie created a separate list of breakout apps, which those with the largest absolute growth in downloads between 2018 and 2019.

So is DuckDuckGo no good? Surprised you did not mention it. Following last week's article about privacy and surveillance capitalism, several readers wrote in about the absence of DuckDuckGo, and it was mentioned a dozen times in the comments. I have suggested this privacy-oriented search engine a few times since 2012, and I think it's worth a go. However, I'm answering Murray's earlier query along the same lines because I can use his email verbatim rather than cobbling together a joint question from multiple sources.

Brave said it has seen a surge in user adoption since releasing version 1.0 of the privacy-centric browser on November 13, 2019. Monthly active users (MAU) have doubled in a year to 10.4 million as of the end of last month. Daily active users of the browser created by Mozilla founder Brendan Eich have tripled in the last year to 3.3 million, the company said Friday. Brave Ads are structured to serve only to users that opt-in to the Brave Rewards program and agree to see ads. Users can then accumulate Brave's Basic Attention Token (BAT), which is a blockchain-based system.

In her popular book, Weapons of Math Destruction, data scientist Cathy O'Neil elegantly describes to the general population the danger of the data science revolution in decision making. She describes how the US News ranking of universities, which orders universities based on 15 measured properties, created new dynamics in university behavior, as they adapted to these measures, ultimately resulting in decreased social welfare. Unfortunately, the idea that data science-related algorithms, such as ranking, cause changes in behavior, and that this dynamic may lead to socially inferior outcomes, is dominant in our new online economy. Ranking also plays a crucial role in search engines and recommendation systems--two prominent data science applications that we focus on in this article. Recommendation systems endorse items by ranking them using information induced from some context--for example, the Web page a user is currently browsing, a specific application the user is running on her mobile phone, or the time of day.

We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined as a closely connected group of vertices having some dominance over other groups of vertices with the expertise (a set of keywords) matching with the query terms (words or phrases). We first design the KICQ that facilitates users to issue an influential CS query intuitively by using a set of query terms, and predicates (AND or OR). In this context, we propose a novel word-embedding based similarity model that enables semantic community search, which substantially alleviates the limitations of exact keyword based community search. Next, we propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. Finally, we propose two efficient algorithms for searching influential communities in large attributed graphs. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches.