The World Wide Web (WWW) abounds with ever-increasing information on many topics. However, since every user has specific information needs and interests, only a tiny part of the WWW is useful to them. For example, in a family, a mother may wish to "find recipes with salmon as the main ingredient", the father may be interested in "what movie to watch tonight?", and the teenage daughter may be wondering "what is artificial intelligence?". In order for humans to quickly ‘retrieve’ relevant information of interest, they usually search the Web using a search engine such as Google.
Although it sounds simple, information retrieval is a complex field involving many sub-tasks and applications. According to "the father of information retrieval", Gerard Salton, information retrieval is the field concerned with the tasks of structure, analysis, organization, storage, searching, and retrieval of information. Applications include, but are not limited to, web search (i.e., searching the WWW) which is the most common type, where the search is specialized in a specific topic only (e.g., searching for shoes within the football topic implies someone looking for football shoes), enterprise search, which involves searching for documents in a corporate intranet, image search, which is searching for images similar to a given image, product search, which involves searching for products similar to a given product, desktop search, which is searching for relevant files in our personal computer, or mobile search, which typically takes location and time into account. Users can be searching for different kinds of items, such as webpages, emails, scholarly papers, books, news stories, or even social profiles. Furthermore, with the advent of new technologies and modalities like virtual reality, it is likely that the scope of information retrieval will only increase with time.
Regardless of the type of search and the type of the returned item, the goal of every information retrieval algorithm is to take a search query as input, and to quickly find and output a ranked list of relevant items, i.e., items that contain information that the user was looking for. For example, in our family example, the mother may submit a query of the form "find recipes with salmon" and the expected result is an ordered (ranked) list of recipes containing salmon, ordered by how relevant each recipe is to the query. Although a straightforward approach would be for a retrieval algorithm to simply compare the query text with the recipe text, this approach will not always work due to language ambiguity. For example, when someone submits a query containing the single word "jaguar" it is very difficult for any algorithm to determine whether the user is looking for documents about jaguar the animal or jaguar the vehicle brand. To be effective, an information retrieval system needs to pay special attention to the meaning of queries rather than the actual words used in them.
Along with ambiguity, information retrieval faces a number of important challenges e.g., dealing with unstructured information, ensuring that it takes each user's context and expectations into account when returning the results, and dealing with scalability (e.g., search engines typically index and search almost instantly, billions of items, in order to answer each user's query, along with answering more than a trillion queries per year). Researchers are continuing to address these challenges.
- Pigi Kouki
Instagram is testing a hidden "Usage Insights" feature, which shows users how much time they spend in the app. This feature was discovered by a computer science student who has a history of uncovering new features in Instagram before they're rolled out to the public. All that we have to go on at this time is the screenshot shared by Jade M. Wong, so it's unclear how detailed these insights are. Instagram is testing "Usage Insights" to show the amount of time users have spent on the app Be self-aware or be prepared to be ashamed for Instagram addiction pic.twitter.com/WzyRGWIOgZ It's also unclear whether or not this feature will be widely released, although it's not something that's out of the realm of possibility.
Today we are starting a six-part series on Negative SEO. The series will be broken into three areas and will show how negative search engine optimization (SEO) has an effect on links, content and user signals. Positive SEO under this broader view would be any tactic performed with the intent to positively impact rankings for a uniform resource locator (URL), and possibly its host domain, by manipulating a variable within the links, content or user signals areas. Negative SEO would be any tactic performed with the intent to negatively impact rankings for a URL, and possibly its host domain, by manipulating a variable within the links, content or user signal buckets. If you can accidentally hurt your rankings by shifting a variable, then it would logically suggest that an external entity shifting that same variable associated with your site could result in a ranking decrease or outright deindexation.
This course centers around the technical steps you need to take to put your online assets (website, blog site, online store, etc.) in the best possible light in the eyes of search engines, more specifically Google and Bing-Yahoo. This course covers thing you absolutely must do to have even a shot at getting to page one of these search engines organically. I follow these lectures with additional guidance to help you construct and display your web page assets so that they not only pass scrutiny when being crawled by "Spiders" in the service of these search engines, but that they receive high-marks from these activities - which will hep you to place higher in the search engine rankings when organic searches are conducted by the public looking for online information. There are many "website designers" out there today completing sites using templates, widgets, etc. And the whole world is out there using similar keywords and keyword phrases trying to get found.
While searching for things over internet, I always wondered, what kind of algorithms might be running behind these search engines which provide us with the most relevant information? How do they decide which result to show for which set of search keywords. This might be a no brainer for a few people, but definitely an interesting problem for some of the best brains around the world. To find the answer, I read every guide, tutorial, learning material that came my way. Information retrieval system is a network of algorithms, which facilitate the search of relevant data / documents as per the user requirement.
Google has introduced new shopping campaigns for AdWords, which utilize automation and machine learning to maximize conversion value. If an advertiser were to define their conversion value as "revenue," for example, then AdWords will automatically optimize the shopping campaign to maximize revenue based on budget constraints. Standard shopping campaigns will continue to be offered along with Google AdWords' new goal-optimized shopping campaigns. Google boasts that the new shopping campaign type "offers a fully-automated solution to drive sales and reach more customers." The new shopping campaigns will be automatically optimized to help marketers achieve their specific goal, whether it's maximizing conversion value or maximizing conversion value at a specific return on ad spend.
We formulate a private learning model to study an intrinsic tradeoff between privacy and query complexity in sequential learning. Our model involves a learner who aims to determine a scalar value, $v^*$, by sequentially querying an external database and receiving binary responses. In the meantime, an adversary observes the learner's queries, though not the responses, and tries to infer from them the value of $v^*$. The objective of the learner is to obtain an accurate estimate of $v^*$ using only a small number of queries, while simultaneously protecting her privacy by making $v^*$ provably difficult to learn for the adversary. Our main results provide tight upper and lower bounds on the learner's query complexity as a function of desired levels of privacy and estimation accuracy. We also construct explicit query strategies whose complexity is optimal up to an additive constant.
Understanding SEO is the first rule for being able to optimize your site and its content so that would-be customers can actually find your company online. When your SEO is on point, you are more likely appear in search engine results when someone looks for the type of products or services you sell. However, to be able to truly understand SEO, you need to comprehend how modern search engines work -- and that means understanding artificial intelligence, or AI. AI is a technological advancement that enables a combination of hardware and software to function like a human brain -- minus the inherent flaws in logic and the relatively small memory capacity. It makes it possible to not only analyze large amounts of data but to draw meaningful insights about the information.
The Home Office is to hold an internal review of its handling of the Windrush scandal, Theresa May has said. She told MPs it would have "full access" to all relevant documents, including policy papers and case files. Her announcement came as Labour prepares to try and force ministers to release all government papers relating to Windrush cases since 2010. Jeremy Corbyn said the crisis had been "made in the Home Office" under Theresa May's leadership. At Prime Minister's Questions, he asked Mrs May whether she "felt a pang of guilt" about the resignation of Amber Rudd over the issue earlier this week.
Initially developed by Facebook, Presto is an open source, distributed ANSI SQL query engine that delivers fast analytic queries against various data sources ranging in size from gigabytes to petabytes. For data scientists, this is ideal for returning Big Data query results in seconds, accelerating the iterative nature of data science discoveries by powering dashboards, reporting and ad-hoc analysis. Presto was designed and built from scratch to be a fast SQL query engine. It follows the classic MPP SQL engine design in which query processing is parallelized over a cluster of machines. As a result, highly concurrent queries execute at interactive speeds.
We propose a simple, robust, and scalable reverse image search engine that leverages convolutional features from Keras' pre-trained neural networks and the distance metric from Scikit-Learn's K-Nearest Neighbors. We show example queries using data scraped from Google images, and dive deeper in how we use the search engine to track the proliferation of memes from the dark web.