Online retailers face a whole host of relatively unique search engine optimization (SEO) considerations most other websites don't deal with. Most discussions about those differences focus on issues such as tags, uniform resource locators (URLs), link structure, duplicate content and so on. In this post, I want to zoom out a bit and start talking about strategic approaches. I've decided to focus specifically on three factors that deserve special attention in developing an SEO strategy for online retailers: keyword research, mobile crawling and customer reviews. This is by no means an exhaustive list, but I believe it's a useful starting point.

If you want to take your SEO to the next level – or even keep up – in 2018 and beyond, then you need to understand how artificial intelligence (AI) is shaping SEO, and how you can use this knowledge to boost your rankings. Up until recently, search engine algorithms were entirely hand-coded by engineers but this has its limitations, not least because of the sheer size of the task and the potential for human error. Artificial intelligence such as speech recognition and image classification software has helped to pave the way for integrating'machine learning' into search engine algorithms. Now new technologies are enabling engineers to push the boundaries even further. Artificial intelligence presents an opportunity to create an algorithm that learns from the behaviour of searchers and, ultimately, refines itself with minimal human input, if any. With the help of AI, search engines can consider factors such as your location, your search history, your favourite websites, and what other users click on for a similar query to give you the most appropriate search results for your individual needs. The AI can then analyse your behaviour in response to a particular search and how you interact with the results, and then improve what it offers the next time someone makes the same search.

Search Engine Optimisation refers to the science of making sure that your website comes on top of the search results to attract your target audience. SEO has its own syntax- it does not involve stuffing keywords or having many product pages to spank up your ranking. If you do this, the chances are high that you will not achieve the desired results. Your pages should have a mobile-friendly interface and feature relevant keywords. Every now and then, SEO techniques keep changing.

If you run a website and would like to research keywords, there are some best practices that are recommended to help you find the best keyword phrases to use. In this article, we'll focus on the several research methods and provide hints on how to carry out effective keyword research. Keep in mind that getting ranked for a keyword phrase depends on how relevant the content is around the much sought-after phrase. Choosing the right keywords can easily get overwhelming in your quest to create the best SEO strategies. That is why we came up with this step by step guide that you can use to find the best keywords for your website.

At Las Vegas gadget show CES, Google is showcasing new features of its voice-enabled digital assistant including a language translator. We use this phrase every day. Once upon a time, the word "Google" just indicated a very long number. That extends to physical products as well. But the world's most powerful search engine can do more than find things.

Google's logo is seen on a building in Irvine, California. We use this phrase every day. Once upon a time, the word "Google" just indicated a very long number. That extends to physical products as well, like these 20 incredibly useful Google products and services you probably didn't know about until now. But the world's most powerful search engine can do more than find things.

Supervised machine learning methods usually require a large set of labeled examples for model training. However, in many real applications, there are plentiful unlabeled data but limited labeled data; and the acquisition of labels is costly. Active learning (AL) reduces the labeling cost by iteratively selecting the most valuable data to query their labels from the annotator. This article introduces a Python toobox ALiPy for active learning. ALiPy provides a module based implementation of active learning framework, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods. In the toolbox, multiple options are available for each component of the learning framework, including data process, active selection, label query, results visualization, etc. In addition to the implementations of more than 20 state-of-the-art active learning algorithms, ALiPy also supports users to easily configure and implement their own approaches under different active learning settings, such as AL for multi-label data, AL with noisy annotators, AL with different costs and so on. The toolbox is well-documented and open-source on Github, and can be easily installed through PyPI.

Microsoft's Bing search engine shows results for sickening child pornography images, research has found. The disturbing revelation discovered that it was easy to find illegal photos of under-age boys and girls on the site. Image searches for'porn kids,' 'porn CP' (a known abbreviation for'child pornography') and'nude family kids' all produced the exploitative content. People looking for the horrific content only needed to turn off SafeSearch filter to find the imagery. An investigation commissioned by TechCrunch found that Bing also suggested other disturbing phrases to help paedophiles target children.

Google is offering US schoolchildren the chance to design their own Doodle to appear on its homepage. The Google Doodle sees the Silicon Valley search giant periodically replace its familiar logo with a sketch, often animated, to celebrate a public figure on an anniversary associated with them or their achievements. Doing so offers an opportunity to champion figures from the arts and sciences who have distinguished themselves through innovation or by blazing a trail for others and deserve to be better known. This year's theme is "hope", with entrants asked to submit a design based on their personal wishes for the future. Kids who would like to get involved have until 8pm Pacific Time on 19 March 2019 to upload a .jpg

We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment. Comparing with the previous work, the new framework recovers the state-of-the-art dynamic regret, but does not require extra gradient queries for every iteration. Specifically, when functions are $\alpha$ strongly convex and $\beta$ smooth, to achieve the state-of-the-art dynamic regret, the previous work requires $O(\kappa)$ with $\kappa = \frac{\beta}{\alpha}$ queries of gradients at every iteration. But, our framework shows that the query complexity can be improved to be $O(1)$, which does not depend on $\kappa$. The improvement is significant for ill-conditioned problems because that their objective function usually has a large $\kappa$.