Semantic search. Guha et al. distinguish two major forms of search: navigational and research.[3] In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. Semantic search is not applicable to navigational searches. In research search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. There is no particular document which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents which together will provide the desired information. Semantic search lends itself well with this approach that is closely related with exploratory search. Rather than using ranking algorithms such as Google's PageRank to predict relevancy, semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. Disambiguation[edit] Commonly used searching methodologies[edit]
Seth Grimes. Information technology strategy consulting Seth Grimes is the leading industry analyst covering text analytics, sentiment analysis, and analysis on the confluence of structured and unstructured data sources. He founded Washington DC based Alta Plana Corporation, an information technology strategy consultancy, in 1997 and is longtime TechWeb contributor (InformationWeek, AllAnalytics, Internet Evolution, and before them, Intelligent Enterprise). He created and organizes the twice-yearly Sentiment Analysis Symposium and was founding chair of the Text Analytics Summit (2005-13).
Seth consults, writes, and speaks on business intelligence, data management and analysis systems, text mining, visualization, and related topics. Follow Seth on Twitter at @SethGrimes. Grimes earned a master's in mathematics at the Univ. of Washington in Seattle and a bachelor's in mathematics and philosophy at Wesleyan Univ. in Middletown, Connecticut. Interviewed Quoted. Recommender system. Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item.[1][2] Recommender systems have become extremely common in recent years, and are applied in a variety of applications.
The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are also recommender systems for experts, jokes, restaurants, financial services, life insurance, persons (online dating), and twitter followers .[3] Overview[edit] The differences between collaborative and content-based filtering can be demonstrated by comparing two popular music recommender systems - Last.fm and Pandora Radio. Each type of system has its own strengths and weaknesses. Recommender system is an active research area in the data mining and machine learning areas.
Hunch. Gillespie - The Relevance of Algorithms. Participatory Culture and the New Governance of Communication: The Paradox of Participatory Media. This article develops a critical alternative to the common equation between participatory culture and democratic communication and argues that power on online participatory platforms should be understood as the governance of semiotic open-endedness. This article argues that the concept of cultural expression cannot be understood solely by looking at users’ cultural practices, but should be revisited to pay attention to the networked conditions that enable it. This involves tracing the governance of disparate processes such as protocols, software, linguistic processes, and cultural practices that make the production and circulation of meaning possible. Thus, communication on participatory platforms should be understood as the management of flows of meaning, that is, as the processes of codification of the informational, technical, cultural, and semiotic dynamics through which meanings are expressed.
. © 2012 SAGE Publications, Inc. 200507eass. Google. Google Now. History[edit] In late 2011, reports surfaced that Google was greatly enhancing their product Google Voice Search for the next version of Android. It was originally code named "Majel" after Majel Barrett, the wife of Gene Roddenberry, and well known as the voice of computer systems in his Star Trek franchise; it was also codenamed "assistant".[4] On June 27, 2012, Google Now was unveiled as part of the premier demonstration of Android 4.1 Jelly Bean at the Google I/O.[5] On October 29, 2012, Google Now received an update through the Google Play Store bringing the addition of Gmail cards.[6] Google Now displays cards with information pulled from the user's Gmail account, such as flight information, package tracking information, hotel reservations and restaurant reservations. Other additions were movies, concerts, stocks and news cards based on the users location and search history.
Also included is creating calendar events using voice input. Functionality[edit] Reception[edit] See also[edit] Just a guy in a garage. One of my daughter's friends suggested that sequels would, on average, recieve lower scores than the original movies - as, at least in her experience, they were invariably worse. I thought I'd just confirm her suspicions so that I could let her know that she was thinking about the problem in a good way. However, to my surprise the opposite appears to be true. Here is the mean score - the 0.5879992 number (adjusted for various things) for each episode of Sex in the City. Sex and the City: Season 1 0.5879992 41138Sex and the City: Season 2 0.5824835 43795Sex and the City: Season 3 0.6523933 38983Sex and the City: Season 4 0.7066851 34616Sex and the City: Season 5 0.7359862 33380Sex and the City: Season 6: Part 1 0.8097552 33532Sex and the City: Season 6: Part 2 0.8241694 27914 As you can see the later the sequel the better the result.
This seems, at least to me, counter intuitive - However the answer may lie in the second number which is the number of people who rated the movie. Products | RecSys. Latest Digital Marketing Trends, Insights and News from SmartFocus. In previous posts, I’ve looked at what personalization was not. In the last post of this series, I’m looking at the future of personalization – and how you should approach it. Part 3: The future of Ecommerce Personalization “Groupon knows that targeting by regions increases conversion and sales, but imagine how much they could amplify that effect if they were targeting based on a rich and sophisticated understanding of the individual person that receives each offer?” (Techcrunch) Online retailers should be itching to move beyond recommendations into personalization. Here are just a few examples of where personalization can make a difference and change the face of ecommerce.
Product Recommendations Initially, driving product recommendation areas with personalization logic will lead to much more relevant product suggestions. I think that crowd-based recommendations have their place in a personalization strategy. Search Emails Social What’s next for Personalization? Experience - knowledge - prediction.
In the News/Media. Aug 2007: "Super Crunchers...are delivering staggeringly accurate results" Super Crunchers - Why Thinking-By-Numbers Is The New Way To Be Smart. Yale law professor and noted econometrician and commentator, Ian Ayres' new book has been embraced by the "best and brightest" business thinkers. With a fast-moving and compelling style, Ayres shows the value of evidence-based decision-making in various arenas. In the world of movies, Ayres identifies and highlights the value that Epagogix adds to film-makers, film financiers -- and film audiences. (English) Super Crunchers is published by Bantam Books, a Division of Random House Inc. 16 Oct 2006: What if you built a machine to predict hit movies?
(English) 08 Oct 2006: "This is an incredibly valuable tool" Noted American writer Malcolm Gladwell at the podium at the annual New Yorker Magazine Festival. 27 Mar 2014: Epagogix presenting at Password - leading Baltic marketing conference (English) (English and Spanish) 06 Aug 2013: Hi Ho!... (English) L'Echo_Belgium_04_Jan_2013. Casualty Actuarial Society | Casualty Actuarial Society. Research and Development: Client-side recommendations. Facebook looking for meaning in user posts with 'deep learning' algorithms. (Phys.org) —Officials at Facebook have apparently decided to get serious about making sense of posts by its vast user base—according to MIT's Technology Review, officials with the company (specifically Chief Technical Officer Mike Schroepfer) have announced that they have put together a team of eight professionals with the mission of developing what the software industry has begun calling "deep learning. " Deep learning is a type of software programming where algorithms are created that allow for building simulated neural networks.
Such neural networks are capable of "learning" by analyzing patterns over time. Facebook, TR reports, is hoping to use its algorithms to better target ads, and also to improve its newsfeed. As anyone who uses Facebook knows, friending people means adding their posts to your personal newsfeed. As the number of friends grows, so too does the number of newsfeed entries.
Facebook will charge to 'promote' user posts (Update) Making Meaning | 15 Meanings. What types of meaningful experiences do people value? In the course of helping companies develop products and services that suit their markets, every year we interview over 100,000 individuals from countries and cultures around the world. In these interviews, we’ve found commonalities among the meanings people feel strongly about, whether we’re studying the adoption of new software in Poland or the purchase of toothbrushes in Florida. We’ve compiled a list of these meanings, but it is far from exhaustive. We’ve found potentially dozens of types of meaningful experiences and at least as many possible ways to characterize them. What we concentrate on here are 15 of the meanings that emerge most frequently in these interviews and appear to be universal among people’s values. While the relative importance of these meaningful experiences might vary and their interpretation could differ slightly, all cultures seem to recognize their significance. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.