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Safe Browsing Tool. Prediction API. Outbrain - Related Link Widgets. About. Meet the Founders A Q&A with Noah and James Why did you build Percolate Noah: “We are excited about technology, brands, and content and thought there was a better, more scalable, way to help brands create the content they need for the social web.”


Why content? James: “As marketers, building a brand has always been about communication. What’s changed to make Percolate relevant? Web Services API. Kleiner-Backed Lockerz Acquires Social Sharing Platform AddToAny. Exclusive-Social commerce network Lockerz has acquired social sharing platform AddToAny.

Kleiner-Backed Lockerz Acquires Social Sharing Platform AddToAny

Financial terms of the acquisition were not disclosed. AddToAny, which has never raised any outside financing and is profitable, allows users to share and bookmark online content with social networks, news aggregators, email services, and instant messengers. The company was one of the first to offer a social sharing and bookmarking widget for website publishers and currently reaches 500 million unique users per month. For monetization, AddToAny sells anonymous aggregate sharing data, which is used by clients to increase the relevancy of their ads. Revenue projections for 2012 ranged in the seven figures. As wee’ve reported in the past, Lockerz revolves around the idea that influencers within a social network can become brand and content advocates and affect the behavior of their friends. Yelp's Review Filter Explained. 6 Tips for Finding Great Content to Share on Twitter.

In the land of Twitter, you are known by what you tweet Finding and sharing great content is the key to establishing yourself as a thought leader in the arena of social media.

6 Tips for Finding Great Content to Share on Twitter

Here are some Twitter tips on how to find and share great content: 1. Do a Twitter search of users to identify and follow thought leaders in your niche. Once you’ve decided what field you would like to establish yourself as a leader in on Twitter, do a Twitter search of users and begin following those who are already sharing content in the space. Your Social TV Guide.

Upload & Share PowerPoint presentations and documents. Fanhattan. The +1 button for websites: recommend content across the web. Since we started rolling out the +1 button in March, you’ve been able to recommend content to your friends and contacts directly from Google search results and ads.

The +1 button for websites: recommend content across the web

But sometimes you want to +1 a page while you’re on it. After all, how do you know you want to suggest that recipe for chocolate flan if you haven’t tried it out yet? Trust Metric. This document briefly describes the technical details of Advogato's trust metric.

Trust Metric

The basic trust metric evaluates a set of peer certificates, resulting in a set of accounts accepted. These certificates are represented as a graph, with each account as a node, and each certificate as a directed edge. The goal of the trust metric is to accept as many valid accounts as possible, while also reducing the impact of attackers. Advogato performs certification to three different levels: Apprentice, Journeyer, and Master. This is actually done by running the basic trust metric three times, using the "level" value in the certificate as a threshold.

BOOKS TOOLBOX: 50+ Sites for Book Lovers. Lulu, a book publishing site, is in the news this week. But there are many more sites for book reviews, self-publishing and exchange. Here are more than 50 of our favorites. Disclosure: Lulu currently has an ad campaign running on Mashable. Book Reviews - Search from thousands of books, buy them online and read excellent reviews. - Users can search and compare prices of new and old books from all major stores. - Users can share reviews of some of their favorite books with others. Personalized Recommendations to Help You Discover the Best of the Web. CAiSE03-Recommendation_Based_Discovery_of_Dynamic_Virtual_Communities. What will you share today? 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.

Recommender system

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 - and Pandora Radio. Automated Text Analytics.

Recommendation’s Engine based on Spread Activation algorithm « Álvaro Brange’s Blog. September 7, 2010 Suggestion graph made with test application Hi, Since last year that I haven’t added any post on my blog, but I would like add new posts.

Recommendation’s Engine based on Spread Activation algorithm « Álvaro Brange’s Blog.

This year I’ll start sharing publishing the work carried out in order to get my degree. The document is currently in spanish. Here is the abstract: Open Source Recommendation engine. Recommendation engine.