On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance.

The internet might seem like a level playing field, but it isn't. Safiya Umoja Noble came face to face with that fact one day when she used Google's search engine to look for subjects her nieces might find interesting. She entered the term "black girls" and came back with pages dominated by pornography. Noble, a USC Annenberg communications professor, was horrified but not surprised. For years she has been arguing that the values of the web reflect its builders--mostly white, Western men--and do not represent minorities and women.

The explosion of user-generated content on the internet during the last decades has left the world of querying multimedia data with unprecedented challenges. There is a demand for this data to be processed and indexed in order to make it available for different types of queries, whilst ensuring acceptable response times. An arguably important task is the retrieval of multimedia objects (e.g. We define two multimedia objects to be visually similar if they depict contents that "look similar" to humans. So far, this task has gained comparatively little research recognition.

This article covers several different methods to help you identify and seize opportunities to promote your site quickly and efficiently. I believe that, when it comes down to it, website promotion works best when based on the Pareto principle – that is, 20% of the pages create 80% of the traffic. This article will help you identify, optimize, and promote your best pages, the 20% that generate most of your traffic. As you probably know already, more and more business owners understand the importance of their website's ranking in Google's search results. As it is the go-to search engine for almost everyone and everything, Google has become the effective reality of the business world.

Google launched its own job search engine -- here's how it works The tech giant recently launched its own job search feature, Google for Jobs. As Business Insider's Matt Weinberger reports, the new feature employs machine learning-trained algorithms to sort and organise job listings from a range of employment sites including LinkedIn, Monster and Glassdoor. So if you decide to find your next gig on Google, you'll have a streamlined place to search and AI technology on your side. Here's 13 tips on how to get started using Google for Jobs: Follow Business Insider UK on Twitter. The Independent's bitcoin group on Facebook is the best place to follow the latest discussions and developments in cryptocurrency.

It was not long ago that Artificial Intelligence (AI) was only in the realm of science fiction. Today, it has become a reality and is only growing more prominent in many different industries every day. This includes the internet as AI in search engine technology has been around for a few years. The algorithms used to rank pages have been affected considerably by AI already and that trend will continue into the foreseeable future. Currently, Google's RankBrain, an AI process used help set search engine rankings, is having a major impact which is only expected to expand.

We consider the task of learning the parameters of a {\em single} component of a mixture model, for the case when we are given {\em side information} about that component, we call this the "search problem" in mixture models. We would like to solve this with computational and sample complexity lower than solving the overall original problem, where one learns parameters of all components. Our main contributions are the development of a simple but general model for the notion of side information, and a corresponding simple matrix-based algorithm for solving the search problem in this general setting. We then specialize this model and algorithm to four common scenarios: Gaussian mixture models, LDA topic models, subspace clustering, and mixed linear regression. For each one of these we show that if (and only if) the side information is informative, we obtain parameter estimates with greater accuracy, and also improved computation complexity than existing moment based mixture model algorithms (e.g. tensor methods). We also illustrate several natural ways one can obtain such side information, for specific problem instances. Our experiments on real data sets (NY Times, Yelp, BSDS500) further demonstrate the practicality of our algorithms showing significant improvement in runtime and accuracy.

When we first started hearing about machine learning in the early 2010s, it seemed scary at first. Machine learning is essentially using algorithms to calculate trends, value, or other characteristics of specific things based on historical data. Google has even declared itself a machine learning-first company. If you want to learn more about the tactical side of this technology, Eric Enge has a great write-up on Moz explaining how machine learning impacts SEO from a mathematical standpoint. Search engines like to always experiment with how they can use this evolving technology, but here are nine ways we know that they are currently using machine learning and how it relates to SEO or digital marketing.

We are finally going to get the code ready for our sequence 2 sequence neural network which is taking a machine learning approach to keyword extraction. This was part of a larger natural language processing project I did last year.