SEO Adapts to Rapidly Changing Algorithms
May 30, 2017
When we ponder the future of search, we consider factors like the rise of “smart” searching—systems that deliver what they know the user wants, instead of what the user wants—and how facial recognition search is progressing. Others look from different angles, though, like the business-oriented Inc., which shares the post, “What is the Future of Search?” Citing SEO expert Baruch Labunski, writer Drew Hendricks looks at how rapid changes to search engines’ ranking algorithms affect search-engine-optimization marketing efforts.
First, companies must realize that it is now essential that their sites play well with mobile devices; Google is making mobile indexing a priority. We learn that the rise of virtual assistants raises the stakes—voice-controlled searches only return the very first search result. (A reason, in my opinion, to use them sparingly for online searches.) The article pays the most attention, though, to addressing local search. Hendricks advises:
By combining the highly specific locational data that’s available from consumers searching on mobile, alongside Google’s already in-progress goal of customizing results by location for all users, positioning your brand to those who are physically near you will become crucial in 2017. …
Our jobs as brand managers and promoters will continue to become more complicated as time passes. The days of search engine algorithms filtering by obvious data points, or being easily manipulated, are over. The new fact of search engine optimization is appealing to your immediate markets – those around you and those who are searching directly for your product.
Listing one’s location(s) on myriad review sites and Google Places and placing the address on the company website are advised. The piece concludes by reassuring marketers that, as long as they make careful choices, they can successfully navigate the rapid changes to Google and other online search engines.
Cynthia Murrell, May 30, 2017
Elastic Search Redefining Enterprise Search Landscape
May 24, 2017
Open source enterprise search engine Elastic Search is changing the way large IT enterprises are enabling its user to search relevant data in a seamless manner.
Apiumhub in an in-depth report titled Elastic Search; Advantages, Case Studies & Books says:
Elastic search is able to achieve fast search responses because, instead of searching the text directly, it searches an index instead. This is more or less like searching for a keyword by scanning the index at the back of a book, as opposed to searching every word of every page of the book.
The search engine is easily scalable and can accommodate petabytes of data on multiple servers in short time. Considering it is based on Lucene, developers too find it easy to work with. Even if the keywords are misspelled, the search engine will correct the error and deliver accurate results.
At present, large organizations like Tesco, Wikipedia, Facebook, LinkedIn and Salesforce have already deployed the enterprise search engine across their servers. With the advent of voice-based search, capabilities of Elastic search will be in more demand in the near future, experts say.
Vishol Ingole, May 24, 2017
Your Tax Information Might Be for Sale on Dark Web
May 23, 2017
Theft of personal and sensitive information continues to be a threat for Internet users. Tax information is available for sale for as low as $30 in bulk over Dark Web.
WTMJ-TV published a news report titled Officials Say Thieves Are Stealing Tax Info and Selling It on the Dark Web says:
It may be past tax time, but that doesn’t mean the stress is over. Experts say thieves are stealing W-2 information and selling it on the part of the Internet hidden from search engines known as the dark web.
In this particular instance, the culprit masquerading as a high-level company executive asked the clerk at a company office to mail all W-2 forms. Though the con was discovered immediately, albeit it was too late.
Despite strict IT security policies, data thieves manage to steal sensitive information using a technique called as social engineering. This includes gathering bits and pieces of information from multiple employees and using it together to con someone higher-up for stealing the information. Experts are of the opinion that prevention is the only protection in such cases.
Vishol Ingole, May 23, 2017
Russia Compels Google to Relinquish Default Search-Engine Status on Android
May 11, 2017
Russia has successfully pushed Google into playing fair (on one matter, anyway), we learn from “Google Agrees to Open Android to Other Search Engines in Russia” at the Verge. Writer Jacob Kastrenakes reveals:
In addition to paying a $7.8 million fine, Google has agreed to stop preventing phone manufacturers from changing the default search engine to anything but Google. Google won’t be allowed to require any app exclusivity on new phones, nor will it be allowed to prevent other companies’ apps from coming preinstalled.
While Android is an open platform, core parts of the operating system aren’t, including Google’s app store. That’s allowed Google to set strict conditions for any phone manufacturer that wants to build a phone with access to the Play Store’s millions of apps. Russia’s Federal Antimonopoly Service said this counted as an abuse of Google’s dominant market position, and for the past two years, it’s been investigating and suing over the company’s restrictive terms.
Naturally, Russian search giant Yandex stands to gain from the concession. We can expect that company to negotiate with Android-phone manufacturers to have their search engine preinstalled within Russia. In fact, Yandex’s founder and CEO issued a statement celebrating the settlement, noting that “competition breeds innovation.” Indeed.
Russian Android users will soon be empowered to reject Google Search, too. The company promises a to implement a widget for Chrome that will enable users to set a non-Google search engine as their default. The caveat— prospective engines must sign a commercial agreement with Google. After all, that global near-monopoly will not relinquish any more control than it must.
Cynthia Murrell, May 11, 2017
Machine Learning Going Through a Phase
May 10, 2017
People think that machine learning is like an algorithm magic wand. It works by some writing the algorithmic code, popping in the data, and the computer learns how to do a task. It is not that easy. The Bitext blog reveals that machine learning needs assistance in the post, “How Phrase Structure Can Help Machine Learning For Text Analysis.”
Machine learning techniques used for text analysis are not that accurate. The post explains that instead of learning the meaning of words in a sentence according to its structure, all the words are tossed into a bag and translated individually. The context and meaning are lost. A real world example is Chinese and Japanese because they use kanji (pictorial symbols representing words). Chinese and Japanese are two languages, where a kanji’s meaning changes based on the context. The result is that both languages have a lot of puns and are a nightmare for text analytics.
As you can imagine there are problems in Germanic and Latin-based languages too:
Ignoring the structure of a sentence can lead to various types of analysis problems. The most common one is incorrectly assigning similarity to two unrelated phrases such as “Social Security in the Media” and “Security in Social Media” just because they use the same words (although with a different structure).
Besides, this approach has stronger effects for certain types of “special” words like “not” or “if”. In a sentence like “I would recommend this phone if the screen was bigger”, we don’t have a recommendation for the phone, but this could be the output of many text analysis tools, given that we have the words “recommendation” and “phone”, and given that the connection between “if” and “recommend” is not detected.
If you rely solely on the “bag of words” approach for text analysis the problems only get worse. That is why it phrase structure is very important for text and sentiment analysis. Bitext incorporates phrase structure and other techniques in their analytics platform used by a large search engine company and another tech company that likes fruit.
Whitney Grace, May 10, 2017
Microsoft Offers Android Users a (Weak) Bing Incentive
May 4, 2017
It looks like Microsoft has stooped to buying traffic for Bing; that cannot bode well. OnMSFT reports, “Set Bing as Your Search Engine on iPhone or Android, Get a Microsoft Rewards $5 Gift Card.” Paradoxically, they don’t seem terribly anxious to spread the word. Reporter Kareem Anderson writes:
Sleuthers over in the Reddit forums have dug up a neat little nugget of savings for iPhone and Android users. According to a thread at the Xbox One subreddit, iPhone and Android users who set their default search engine to Bing can receive a Microsoft Rewards $5 gift card. The details were originally pulled from a Microsoft site instructing users on how to make the change from Google to Bing on smartphone devices. We should note that the redemption process hasn’t been without its issues as several Android users have mentioned that it has not worked or appears delayed in confirming the release of gift cards.
So, they’ve created an incentive, but are not promoting it or, apparently, fulfilling it effectively—talk about mixed messages! Still, if you use an Android device and are inclined toward Bing, but haven’t yet set it as your default browser, you may be able to profit a little by doing so. Anderson shares a link to the Microsoft Rewards page for our convenience.
Cynthia Murrell, May 4, 2017
Enterprise Search and a Chimera: Analytical Engines
May 1, 2017
I put on my steam punk outfit before reading “Leading Analytical Engines for Enterprise Search.” Now there was one small factual error; specifically, the Google Search Appliance is a goner. When it was alive and tended to by authorized partners, it was not particularly adept at doing “analytical engine” type things.
What about the rest of the article? Well, I found it amusing.
Let me get to the good stuff and then deal with the nasty reality which confronts the folks who continue to pump money into enterprise search.
What companies does this “real journalism” out identify as purveyors of high top shoes for search. Yikes, sorry. I meant to say enterprise search systems which do analytical engine things.
Here’s the line up:
The Google Search Appliance. As noted, this is a goner. Yep, the Google threw in the towel. Lots of reasons, but my sources say, cost of sales was a consideration. Oh, and there were a couple of Google FTEs plus assorted costs for dealing with those annoyed with the product’s performance, relevance, customization, etc. Anyway. Museum piece.
Microsoft SharePoint. I find this a side splitter. Microsoft SharePoint is many things. In fact, armed with Visual Studio one can actually make the system work in a useful manner. Don’t tell the HR folks who wonder why certified SharePoint experts chew up a chunk of the budget and “fast.” Insider joke. Yeah, Excel is the go to analysis tool no matter what others may say. The challenge is to get the Excel thing to interact in a speedy, useful way with whatever the SharePoint administrator has managed to get working in a reliable way. Nuff said.
Coveo. Interesting addition to the list because Coveo is doing the free search thing, the Salesforce thing, the enterprise search thing, the customer support thing, and I think a bunch of other things. The Canadian outfit wants to do more than surf on government inducements, investors’ trust and money, and a key word based system. So it’s analytical engine time. I am not sure how the wrappers required to make key word search do analytics help out performance, but the company says it is an “analytical engine.” So be it.
Attivio. This is an interesting addition. The company emerged from some “fast” movers and shakers. The baseball data demo was nifty about six years ago. Now the company does search, publishing, analytics, etc. The shift from search to analytical engine is somewhat credible. The challenge the company faces is closing deals and generating sustainable revenue. There is that thing called “open source”. A clever programmer can integrate Lucene (Elasticsearch), use its open source components, and maybe dabble with Ikanow. The result? Perhaps an Attivio killer? Who knows.
Lucidworks (Really?). Yep, this is the Avis to the Hertz in the open source commercial sector. Lucidworks (Really?) is now just about everything sort of associated with Big Data, search, smart software, etc. The clear Lucid problem is Shay Bannon and Elastic. Not only does Elastic have more venture money, Elastic has more deployments and, based on information available to me, more revenue, partners, and clout in the open source world. Lucidworks (Really?) has a track record of executive and founder turnover and the thrill of watching Amazon benefit from a former Lucid employee’s inputs. Exciting. Really?
So what do I think of this article in CIO Review? Two things:
- It is not too helpful to me and those looking for search solutions in Harrod’s Creek, Kentucky. The reason? The GSA error and gasping effort to make key word search into something hot and cool. “Analytical engines” does not rev my motor. In fact, it does not turn over.
- CIO Review does not want me to copy a quote from the write up. Tip to CIO Review. Anyone can copy wildly crazy analytical engines article by viewing source and copying the somewhat uninteresting content.
Stephen E Arnold, May 1, 2017
Creative Commons Eludes Copyright With Free Image Search
April 7, 2017
One scandal that plagues the Internet is improper usage and citation of digital images. Photographs, art, memes, and GIFs are stolen on a daily basis and original owners are often denied compensation or credit. Most of the time, usage is completely innocent; other times it is blatant theft. If you need images for your Web site or project, but do not want to be sent a cease and desist letter or slammed with a lawsuit check out the Creative Commons, a community where users post photos, art, videos, and more free of copyright control as long as you give credit to the original purveyor. Forbes wrote that, “Creative Commons’ New Search Engine Makes It Easy To Find Free-To-Use Images.”
The brand new Creative Commons search engine is something the Internet has waited for:
The Creative Commons search engine gives you access to over nine million images drawn from 500px, Flickr, the Metropolitan Museum of Art, the New York Public Library and the Rijksmuseum. You can search through all or any combination of these collections. You can also constrain your search to titles, creators, tags or any combination of the three. Finally, you can limit your search to images that you can modify, adapt or build upon as you see fit, or that are free to use for commercial purposes.
Creative Commons is a wonderful organization and copyright tool that allows people to share their work with others while receiving proper credit. It is also a boon for others who need photos and video to augment their own work. My only question is: why did it take so long for the Creative Commons to make this search engine?
Whitney Grace, April 7, 2017
Yandex Incorporates Semantic Search
March 15, 2017
Apparently ahead of a rumored IPO launch, Russian search firm Yandex is introducing “Spectrum,” a semantic search feature. We learn of the development from “Russian Search Engine Yandex Gets a Semantic Injection” at the Association of Internet Research Specialists’ Articles Share pages. Writer Wushe Zhiyang observes that, though Yandex claims Spectrum can read users’ minds, the tech appears to be a mix of semantic technology and machine learning. He specifies:
The system analyses users’ searches and identifies objects like personal names, films or cars. Each object is then classified into one or more categories, e.g. ‘film’, ‘car’, ‘medicine’. For each category there is a range of search intents. [For example] the ‘product’ category will have search intents such as buy something or read customer reviews. So we have a degree of natural language processing, taxonomy, all tied into ‘intent’, which sounds like a very good recipe for highly efficient advertising.
But what if a search query has many potential meanings? Yandex says that Spectrum is able to choose the category and the range of potential user intents for each query to match a user’s expectations as close as possible. It does this by looking at historic search patterns. If the majority of users searching for ‘gone with the wind’ expect to find a film, the majority of search results will be about the film, not the book.
As users’ interests and intents tend to change, the system performs query analysis several times a week’, says Yandex. This amounts to Spectrum analysing about five billion search queries.”
Yandex has been busy. The site recently partnered with VKontakte, Russia’s largest social network, and plans to surface public-facing parts of VKontakte user profiles, in real time, in Yandex searches. If the rumors of a plan to go public are true, will these added features help make Yandex’s IPO a success?
Cynthia Murrell, March 15, 2017
Yandex Finally Catches the Long-Tailed Queries
March 7, 2017
One of the happiest moments in a dog’s life is when, after having spent countless hours spinning in circles, is when they catch their tail. They wag for joy, even though they are chomping on their own happiness. When search engines were finally programmed to handle long-tailed queries, that is queries with a lot of words such as a question, people’s happiness was akin to a dog catching their tail. Google released RankBrain to handle long-winded ad NLP queries, but Yandex just released their own algorithm to handle questions, “Yandex Launches New Algorithm Named Palekh To Improve Search Results For Long-Tail Queries” from AIRS Association.
Yandex is Russia’s most-used search engine and in order to improve the user experience, they released Palekh to better process long-tail queries. Palekh, like RankBrain, will bring the search engine closer to understanding the natural language or the common vernacular. Yandex decided on the name Palekh, because the Russian city of the same name has a firebird on its coat of arms. The firebird has a long-tail, so the name fits perfectly.
Yandex handles more than 100 million queries per day that fall under the long-tail query umbrella. When asked if Yandex based Palekh on RankBrain, Yandex only responded that the two algorithms are similar in their purposes. Yandex also uses machine learning to build neural networks to build a smarter search engine:
Yandex’s Palekh algorithm has started to use neural networks as one of 1,500 factors of ranking. A Yandex spokesperson told us they have “managed to teach our neural networks to see the connections between a query and a document even if they don’t contain common words.” They did this by “converting the words from billions of search queries into numbers (with groups of 300 each) and putting them in 300-dimensional space — now every document has its own vector in that space,” they told us. “If the numbers of a query and numbers of a document are near each other in that space, then the result is relevant,” they added.”
Yandex is one of Google’s biggest rivals and it does not come as a surprise that they are experimenting with algorithms that will expand machine learning and NLP.
Whitney Grace, March 7, 2017
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