Today, Businesses Have More Ways – And Places – Than Ever To Market Themselves.Your Local Digital Marketing Strategy Should Specifically Target And Appeal To Potential Customers In Your Geographic Area. Many Local Companies Have Used Some Form Of Digital Marketing Online Even If They Are Not Aware Of It.This Is An Important Local Digital Marketing Tip For Any Business. But For Local Businesses, It Can Be Even More Essential. Customers Who Are Looking For A Restaurant, Store Or Other Local Business Are Likely To Do A Search On Their Phone Or Mobile Device. If You Don't Have A Mobile Optimized Site, Not Only Will It Be Difficult For Them To Interact With Your Site, But It Will Also Be Difficult For Them To Find It In The First Place. If You Want Local Customers, Either On Mobile Or Desktop, To Find You, You Have To Have A Comprehensive Search Strategy.

Suppose, we are given a set of $n$ elements to be clustered into $k$ (unknown) clusters, and an oracle/expert labeler that can interactively answer pair-wise queries of the form, "do two elements $u$ and $v$ belong to the same cluster?". The goal is to recover the optimum clustering by asking the minimum number of queries. In this paper, we initiate a rigorous theoretical study of this basic problem of query complexity of interactive clustering, and provide strong information theoretic lower bounds, as well as nearly matching upper bounds. Most clustering problems come with a similarity matrix, which is used by an automated process to cluster similar points together. Our main contribution in this paper is to show the dramatic power of side information aka similarity matrix on reducing the query complexity of clustering. A similarity matrix represents noisy pair-wise relationships such as one computed by some function on attributes of the elements. A natural noisy model is where similarity values are drawn independently from some arbitrary probability distribution $f_+$ when the underlying pair of elements belong to the same cluster, and from some $f_-$ otherwise. We show that given such a similarity matrix, the query complexity reduces drastically from $\Theta(nk)$ (no similarity matrix) to $O(\frac{k^2\log{n}}{\cH^2(f_+\|f_-)})$ where $\cH^2$ denotes the squared Hellinger divergence. Moreover, this is also information-theoretic optimal within an $O(\log{n})$ factor. Our algorithms are all efficient, and parameter free, i.e., they work without any knowledge of $k, f_+$ and $f_-$, and only depend logarithmically with $n$. Along the way, our work also reveals intriguing connection to popular community detection models such as the {\em stochastic block model}, significantly generalizes them, and opens up many venues for interesting future research.

As most database users cannot precisely express their information needs, it is challenging for database management systems to understand them. We propose a novel formal framework for representing and understanding information needs in database querying and exploration. Our framework considers querying as a collaboration between the user and the database management system to establish a it mutual language for representing information needs. We formalize this collaboration as a signaling game, where each mutual language is an equilibrium for the game. A query interface is more effective if it establishes a less ambiguous mutual language faster. We discuss some equilibria, strategies, and the convergence in this game. In particular, we propose a reinforcement learning mechanism and analyze it within our framework. We prove that this adaptation mechanism for the query interface improves the effectiveness of answering queries stochastically speaking, and converges almost surely. We extend out results for the cases that the user also modifies her strategy during the interaction.

In this paper, we initiate a rigorous theoretical study of clustering with noisy queries (or a faulty oracle). Given a set of $n$ elements, our goal is to recover the true clustering by asking minimum number of pairwise queries to an oracle. Oracle can answer queries of the form : "do elements $u$ and $v$ belong to the same cluster?" -- the queries can be asked interactively (adaptive queries), or non-adaptively up-front, but its answer can be erroneous with probability $p$. In this paper, we provide the first information theoretic lower bound on the number of queries for clustering with noisy oracle in both situations. We design novel algorithms that closely match this query complexity lower bound, even when the number of clusters is unknown. Moreover, we design computationally efficient algorithms both for the adaptive and non-adaptive settings. The problem captures/generalizes multiple application scenarios. It is directly motivated by the growing body of work that use crowdsourcing for {\em entity resolution}, a fundamental and challenging data mining task aimed to identify all records in a database referring to the same entity. Here crowd represents the noisy oracle, and the number of queries directly relates to the cost of crowdsourcing. Another application comes from the problem of {\em sign edge prediction} in social network, where social interactions can be both positive and negative, and one must identify the sign of all pair-wise interactions by querying a few pairs. Furthermore, clustering with noisy oracle is intimately connected to correlation clustering, leading to improvement therein. Finally, it introduces a new direction of study in the popular {\em stochastic block model} where one has an incomplete stochastic block model matrix to recover the clusters.

On Tuesday, Google said it will begin serving up help-wanted job descriptions that its search engine collects across the Internet with help from artificial intelligence. Typing in the query "jobs near me," or "jobs as a chief" will return a swath of information. The data will enable users to filter the jobs by industry and location, when they were posted, and employer. The tool aggregates data from sites like LinkedIn, Monster, WayUp, CareerBuilder, and Glassdoor will include employer ratings from former and current and provide the distance for a typical commute to the job locations. It's a departure from the way that Google has aggregated and served information in its search engine in the past.

Google is trying to turn its search engine into an employment engine. Job hunters will be able to go to Google and see help-wanted listings that its search engine collects across the internet. The results will aim to streamline such listings by eliminating duplicate jobs posted on different sites. Google will also show employer ratings from current and former workers, as well as typical commute times to job locations. Google is trying to turn its search engine into an employment engine.

Looking for a new job is getting easier. Google today launched a new jobs search feature right on its search result pages that lets you search for jobs across virtually all of the major online job boards like LinkedIn, Monster, WayUp, DirectEmployers, CareerBuilder and Facebook and others. Google will also include job listings its finds on a company's homepage. The idea here is to give job seekers an easy way to see which jobs are available without having to go to multiple sites only to find duplicate postings and lots of irrelevant jobs. With this new feature, is now available in English on desktop and mobile, all you have to type in is a query like "jobs near me," "writing jobs" or something along those lines and the search result page will show you the new job search widget that lets you see a broad range of jobs.

Google is trying to turn its search engine into an employment engine. Beginning Tuesday, job hunters will be able to go to Google and see help-wanted listings that its search engine collects across the internet. The results will aim to streamline such listings by eliminating duplicate jobs posted on different sites. Google will also show employer ratings from current and former workers, as well as typical commute times to job locations. This detailed jobs information is a departure from the way Google's main search engine has traditionally shown only bare-bones links to various help-wanted sites.

This image provided by Google shows examples of help-wanted listings displayed on a smartphone. Google is trying to turn its search engine into an employment engine. Beginning Tuesday, June 20, 2017, job hunters will be able to go to Google and see help-wanted listings that its search engine is collecting across the internet. Google will also show employer ratings from current and former workers, as well as typical commute times to where a job is located. It's a departure from the the bare-bones links to various help-wanted sites that Google has traditionally shown.

I've never written one personally but I was having a discussion with the author of a great piece here on Search Engine Journal on AI and its impact on search and the question came up: Between machine learning and the limited space available for organic search, is it on its death spiral? Between machine learning, the limited space available for organic search, and the growth of both voice search and personal assistants, is it on its death spiral? To paint the picture of where this is going, let's look at just some of the changes over the past little while: I'm sure you can see the trend: Google is crafting the results layout in a way that minimizes the impact of organic results on commercially intent searchers. Not coincidentally, Google announced their personal assistant being released on all phones running Android 6.0 and above, taking us beyond running simple queries on our phone and onto more complicated communications and interactions with other systems -- all in a conversational manner.