We study the query complexity of Bayesian Private Learning: a learner wishes to locate a random target within an interval by submitting queries, in the presence of an adversary who observes all of her queries but not the responses. How many queries are necessary and sufficient in order for the learner to accurately estimate the target, while simultaneously concealing the target from the adversary? Our main result is a query complexity lower bound that is tight up to the first order. We show that if the learner wants to estimate the target within an error of $\varepsilon$, while ensuring that no adversary estimator can achieve a constant additive error with probability greater than $1/L$, then the query complexity is on the order of $L\log(1/\varepsilon)$, as $\varepsilon \to 0$. Our result demonstrates that increased privacy, as captured by $L$, comes at the expense of a {multiplicative} increase in query complexity. Our proof method builds on Fano's inequality and a family of proportional-sampling estimators. As an illustration of the method's wider applicability, we generalize the complexity lower bound to settings involving high-dimensional linear query learning and partial adversary observation.

Online harassment is a significant social problem. Prevention of online harassment requires rapid detection of harassing, offensive, and negative social media posts. In this paper, we propose the use of word embedding models to identify offensive and harassing social media messages in two aspects: detecting fast-changing topics for more effective data collection and representing word semantics in different domains. We demonstrate with preliminary results that using the GloVe (Global Vectors for Word Representation) model facilitates the discovery of new and relevant keywords to use for data collection and trolling detection. Our paper concludes with a discussion of a research agenda to further develop and test word embedding models for identification of social media harassment and trolling.

You know how Google says you can't "fix" your way back to position one? That seems unhelpful but it's actually useful. It helps one understand what not to waste time on. In my opinion, based on my experience, once you know what not to focus on, it will help you understand more productive areas to focus on. According to Google, fixing things won't help you recover.

Social activities play an important role in people's daily life since they interact. For recommendations based on social activities, it is vital to have not only the activity information but also individuals' social relations. Thanks to the geo-social networks and widespread use of location-aware mobile devices, massive geo-social data is now readily available for exploitation by the recommendation system. In this paper, a novel group recommendation method, called attentive geo-social group recommendation, is proposed to recommend the target user with both activity locations and a group of users that may join the activities. We present an attention mechanism to model the influence of the target user $u_T$ in candidate user groups that satisfy the social constraints. It helps to retrieve the optimal user group and activity topic candidates, as well as explains the group decision-making process. Once the user group and topics are retrieved, a novel efficient spatial query algorithm SPA-DF is employed to determine the activity location under the constraints of the given user group and activity topic candidates. The proposed method is evaluated in real-world datasets and the experimental results show that the proposed model significantly outperforms baseline methods.

Link Building is a part of Search Engine Optimisation and is usually referred to as Off-Page. Unlike, On-Page – Link building is an ongoing process. For the sake of not getting into a debate, I will call "on-page" - a onetime process). Link Building starts as soon as you finish on-page work on the website. In layman's language - Link building is a method to get references from other websites to your website.

Bing announced a new link penalties. These link penalties are focused on taking down private blog networks (PBNs), subdomain leasing and manipulative cross-site linking. An inorganic site structure is a linking pattern that uses internal site-level link signals (with subdomains) or cross-site linking patterns (with external domains) in order to manipulate search engine rankings. While these spam techniques already existed, Bing introduced the concept of calling them "inorganic site structure" in order to describe them. Bing noted that sites legitimately create subdomains to keep different parts of the site separate, such as support.example.com.

Pinterest has updated the look of its app for iOS and Android with a renewed focus on personalized recommendations and a more efficient use of space. The most obvious change when opening the app after the update is the reduced space around pins. Pinterest is cramming the home feed with as much content as it can while still keeping things aesthetically pleasing. The next-most obvious change is the carousel of personal recommendations at the top of the home feed. You can scroll through and tap on one of the topics of interest to open up a dedicated feed.

Searching on Google has become second nature for billions of people. In fact, Googling is so entrenched in our culture, it's become the generic word for looking things up online. Yet even though you long ago mastered the essentials, there's still a lot most of us can learn about how to search faster and more effectively. Senior Google research scientist Daniel M. Russell recently published "The Joy of Search: A Google Insider's Guide Going Beyond The Basics." USA TODAY caught up with Russell in New York where he was teaching a "Grow With Google" class on search strategies.

Slyce was born from a simple vision: What if you could find and buy any product you see, using the technology of visual search on a photograph? Of course, the task of visual product search is born of a more ambitious challenge: Given a query image, how do you determine the most relevant images from a universe of images and presenting them in decreasing order of relevancy? In the case of a traditional search engine, for example, the universe would be all images that are available on the internet. For a social network, it would be all images that were posted or pinned on its website (or app). In our case we elected to focus on solving visual search for retailers, narrowing the universe to be the entire catalog of products that a given retailer sells.

Note: By submitting this form, you agree to Third Door Media's terms. Google is making the largest change to its search system since the company introduced RankBrain, almost five-years ago. The company said this will impact 1 in 10 queries in terms of changing the results that rank for those queries. BERT started rolling out this week and will be fully live shortly. It is rolling out for English language queries now and will expand to other languages in the future.