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Collective Intelligence in Action is a practical book for applying collective ppti.info Collective Intelligence in Action is a hands-on guidebook for implementing collective-intelligence concepts using Java. It is the first Java-based book to. “Rich Dad Poor Dad is a starting point for anyone looking to If you purchase this book without a cover, or purchase Artificial Intelligence and Molecular Biology .

Collective Intelligence In Action Pdf

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Collective Intelligence in Action [Satnam Alag] on ppti.info *FREE* shipping on qualifying offers. There's a great deal of wisdom in a crowd, but how do you. Collective Intelligence in Action Free Pdf Download Collective Intelligence in Action is a hands-on guidebook for implementing collective intelligence concepts . A crowd's collective intelligence will produce better results than those of a . The use of this intelligence to act as a filter for what's valuable in your application for.

Value creation develops through small contributions of the collective. Providing more than four million English-language articles, it is the largest English-language encyclopedia worldwide and represents an example for successful social collaboration and collective intelligence.

This approach has been copied by companies t-systems, web. Other approaches for this purpose are Social Sharing platforms that enable users to store, manage, and share contents, such as bookmarks, videos, photos, etc. Cross-references and categories are supported through tags that enable other users to better understand the user generated content. For the successful design of collective intelligence applications in companies and organizations research has identified first success factors that I will outline briefly Gregg ; Bonabeau : Control: Applying collective intelligence approaches at the same time means a loss of control since previously closed hierarchical structures are opened up and processes are outsourced.

Loss of control can have different effects. Unintended or undesired objectives or solutions may result, the outcome of the activities may be unpredictable, and the accountability and responsibility remain unclear — especially in the case of bad results.

The provision of internal information necessarily involves the question to what extend a company wants to open up to the environment or what kind of restrictions may be affected e. Diversity vs. Engagement: The collective needs motivation for active participation.

NLP At Work: The Difference that Makes the Difference in Business

Incentives monetary or non-monetary allowances may help, but also other motives such as altruism, self-fulfillment, or group identification can be perceived as activation support Leimeister et al. Policing: The more participants are involved the higher is the probability of misconduct or malicious behavior. Punishment can reduce or end this behavior, but it can also have negative effects on other users, leading to a change in individual decision processes or making them leave the collective.

Intellectual Property: If the collective generates ideas and solutions, it is necessary to discuss if and how a company might acquire the intellectual property.

Particularly, this involves the question on whether a participant is willing to hand over his or her intellectual property. This offers a high amount of research questions, ranging from different motives and incentives for active participation of different user and stakeholder groups in collective intelligence applications and respective contingency factors. The effects of different collective intelligence applications, especially for different target groups and tasks as well as the underlying mechanisms, require more in depth analyses.

Scientists from the fields of sociology, mass behavior, and computer science have made important contributions to this field. When a group of individuals collaborate or compete with each other, intelligence or behavior that otherwise didn t exist suddenly emerges; this is commonly known as collective intelligence. The actions or influence of a few individuals slowly spread across the community until the actions become the norm for the 8 What is collective intelligence? To better understand how this circle of influence spreads, let s look at a couple of examples.

In his book The Hundredth Monkey, 1 Ken Keyes recounts an interesting story about how change is propagated in groups. In , on the isolated Japanese island of Koshima, scientists observed a group of monkeys.

They offered them sweet potatoes; the monkeys liked the sweet potatoes but found the taste of dirt and sand on the potatoes unpleasant. One day, an month-old monkey named Imo found a solution to the problem by washing the potato in a nearby stream of water.

She taught this trick to her mother. Her playmates also learned the trick and taught it to their mothers.

Initially, only adults who imitated their children learned the new trick, while the others continued eating the old way. In the autumn of , a number of monkeys were washing their potatoes before eating. The exact number is unknown, but let s say that out of 1,, there were 99 monkeys who washed their potatoes before eating.

Early one sunny morning, a th monkey decided to wash his potato. Then, incredibly, by evening all monkeys were washing their potatoes. The th monkey was that tipping point that caused others to change their habits for the better. Soon it was observed that monkeys on other islands were also washing their potatoes before eating them. As users interact on the web and express their opinions, they influence others.

Their initial circle of influence is the group of individuals that they most interact with. Because the web is a highly connected network of sites, this circle of influence grows and may shape the thoughts of everybody in the group. This circle of influence also grows rapidly throughout the community another example helps illustrate this further. In , as the influenza flu pandemic spread, nearly 14 percent of Fiji s population died in just 16 days.

Nearly one third of the native population in Alaska had a similar fate; it s estimated that worldwide, nearly twenty-five million people died of the flu. A pandemic is a global disease outbreak and spreads from person to person. First, one person is affected, who then transmits it to another and then another.

The newly infected person transmits the flu to others; this causes the disease to spread exponentially.

Collective Intelligence in Action

In its 20 months of existence, YouTube had grown to be one of the busiest sites on the Internet, dishing out million video 2 views a day. It ramped from zero to more than 20 million unique user visits a day, with mainly viral marketing spread from person to person, similar to the way the pandemic flu spreads. In YouTube s case, each time a user uploaded a new video, she was easily able to invite others to view this video.

As those others viewed this video, other related videos popped up as recommendations, keeping the user further engaged. Ultimately, many of these viewers also became submitters and uploaded their own videos as well. As the number of videos increased, the site became more and more attractive for new users to visit As of September 9 6 CHAPTER 1 Understanding collective intelligence Whether you re a budding startup, a recognized market leader, or looking to take an emerging application or web site to the next level, harnessing information from users improves the perceived value of the application to both current and prospective users.

This improved value will not only encourage current users to interact more, but will also attract new users to the application. The value of the application further improves as new users interact with it and contribute more content. This forms a selfreinforcing feedback loop, commonly known as a network effect, which enables wider adoption of the service.

Next, let s look at CI as it applies to web applications. We walk through an example to illustrate how it can be used in web applications, briefly review its benefits, see how it fits in with Web 2.

Let s expand on our earlier definition of collective intelligence.

Collective intelligence of users in essence is The intelligence that s extracted out from the collective set of interactions and contributions made by your users. The use of this intelligence to act as a filter for what s valuable in your application for a user This filter takes into account a user s preferences and interactions to provide relevant information to the user.

This filter could be the simple influence that collective user information has on a user perhaps a rating or a review written about a product, as shown in figure 1. This book is focused toward building the more involved models to personalize your application. As shown in figure 1. You need to 1 Allow users to interact with your site and with each other, learning about each user through their interactions and contributions. Let s walk through an example to understand how collective intelligence can be a catalyst to building a successful web application.

User Intelligence from Mining Data A user influences others by reviews, ratings, recommendations, and blogs A user is influenced by other reviews, ratings, recommendations, and blogs User Figure 1. They re based in Silicon Valley and as is the trend nowadays, they re building their fledgling company without any venture capital on a shoestring budget leveraging open source software.

They believe in fast-iterative agile-based development cycles and aren t afraid to release beta software to gain early feedback on their features. In their first iteration, they launched an application where users mainly friends and family could buy items and view relevant articles. There wasn t much in terms of personalization or user interaction or intelligence a plain vanilla system.

Next, they added the feature of showing a list of top items purchased by users, along with a list of recently purchased items. This is perhaps the simplest form of applying collective intelligence providing information in aggregate to users. To grow the application virally, they also enabled users to these lists to others. Users used this to forward interesting lists of items to their friends, who in turn became users of the application.

In their next iteration, they wanted to learn more about their users. So they built a basic user profile mechanism that contained explicit and implicit profile information.

The explicit information was provided directly by the users as part of their accounts first name, age, and so on. The implicit information was collected from the user interaction data this included information such as the articles and content users viewed and the products they purchased.

They also wanted to show more relevant articles and content to each user, so they built a content-based recommendation engine that analyzed the content of articles keywords, word frequency, location, and so forth to correlate articles with each other and recommend possibly interesting articles to each user. Next, they allowed users to generate content. They gave users the ability to write about their experiences with the products, in essence writing reviews and creating their list of recommendations through both explicit ratings of individual products 3 Note that beta doesn t mean poor quality; it just means that it s incomplete in functionality.

They also gave users the capability to rate items and rate reviews. Ratings and reviews have been shown to influence other users, and numerical rating information is also useful as an input to a collaborative-based recommendation engine. With the growing list of content and products available on the site, John and Jane now found it too cumbersome and expensive to manually maintain the classification of content on their site. The users also provided feedback that content navigation menus were too rigid.

So they introduced dynamic navigation via a tag cloud navigation built by an alphabetical listing of terms, where font size correlates with importance or number of occurrences of a tag. The terms were automatically extracted from the content by analyzing the content.

The application analyzed each user s interaction and provided users with a personalized set of tags for navigating the site. The set of tags changed as the type of content visited by the users changed. Further, the content displayed when a user clicked on a tag varied from user to user and changed over time.

Some tags pulled the data from a search engine, while others from the recommendation engine and external catalogs.

In the next release, they allowed the users to explicitly tag items by adding free text labels, along with saving or bookmarking items of interest. As users started tagging items, John and Jane found that there was a rich set of information that could be derived. First of all, users were providing new terms for the content that made sense to them in essence they built folksonomies.

The process of extracting tags using an automated algorithm could also be enhanced using the dictionary of tags built by the users. These user-added tags were also useful for finding keywords used by an ad-generation engine. They could also use the tags created by users to connect users with each other and with other items of interest. This is collective intelligence in action.

Next, they allowed their users to generate content.

Transactions on Computational Collective Intelligence XXVI

Users could now blog about their experiences, or ask and respond to questions on message boards, or participate in building the application itself by contributing to wikis. John and Jane quickly built an algorithm that could extract tags from the unstructured content. They then matched the interests of users gained from analyzing their interaction in the applications with those of other users to find relevant items. They were soon able to learn enough about their users to personalize the site for each user, and to provide relevant content targeting niche items to niche users.

They could also target relevant advertisements based on the user profile and context of interaction. They also modified the search results to make them more relevant to each user, for which they used the user s profile and interaction history when appropriate.

They customized advertising by using keywords that were relevant to both the user and the page content. To make the application stickier, they started aggregating and indexing external content they would crawl a select list of external web sites to index the content and present 4 Folksonomies are classifications created through the process of users tagging items.

They also connected to sites that tracked the blogosphere, presenting the users with relevant content from what others were saying in blogs.

Collective Intelligence in Action

Data mining: Core concepts of data mining. Using an open source data mining framework: Standard data mining API: Building a text analysis toolkit 8.

Building the text analyzers. Building the text analysis infrastructure. Use cases for applying the framework.

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Discovering patterns with clustering 9. Clustering blog entries. Leveraging WEKA for clustering. Making predictions Classification fundamentals. Classification and regression using JDM. Intelligent search Search fundamentals. Approaches to intelligent search.

Building a recommendation engine Recommendation engine fundamentals. About the book Following a running example in which you harvest and use information from blogs, you learn to develop software that you can embed in your own applications.

What's inside Architecture for embedding intelligence in your application Developing metadata about the user and content Gather intelligence from tagging and build tag clouds Introduction to intelligent web crawling and Nutch Harvesting information from the blogosphere Build a text analysis toolkit leveraging Lucene Business intelligence and data mining for recommendations and promotions Leveraging open-source data mining toolkit WEKA and the Java Data Mining JDM standard Incorporating intelligent search in your application Building a recommendation engine—finding related users and content Real-world case studies of Amazon, Google News, and Netflix personalization.

About the reader This book assumes you have a basic level of Java coding skills. Collective Intelligence in Action combo added to cart.

Your book will ship via to:. Commercial Address. Collective Intelligence in Action eBook added to cart. Don't refresh or navigate away from the page. Robert I.These distortions can be diminished by the following collective intelligence approaches. The interpretation of probability matching here seems a bit suspect, though, since it could possibly be that the fish learn the causal relationship between choosing the wrong option and being presented shortly thereafter with the opportunity to choose the correct option.

The most vivid way for describing the areas of application and potentials of collective intelligence in the context of IT is to consider the core research question of the MIT Center for Collective Intelligence: How can people and computers be connected so that collectively they act more intelligently than any individual, group, or computer has ever done before? Reviews, discussion forum posts, blog entries, and chat sessions are all examples of unstructured data.

Web applications that take this approach develop deeper relationships with their users, provide more value to users who return more often, and ultimately offer more targeted experiences for each user according to her personal need.

Among other things, only data supporting the individual opinion might be used or just simple solutions might be preferred. They also connected to sites that tracked the blogosphere, presenting the users with relevant content from what others were saying in blogs. Although the paper isn't framed this way, it points to a technique for performing inference in agent-based models.

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