background preloader

Recommender system

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.

http://en.wikipedia.org/wiki/Recommender_system

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. Nearest neighbor search Nearest neighbor search (NNS), also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest (or most similar) points. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values. Formally, the nearest-neighbor (NN) search problem is defined as follows: given a set S of points in a space M and a query point q ∈ M, find the closest point in S to q. 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.

k-nearest neighbor algorithm In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression.[1] In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership.

Trust Metric This document briefly describes the technical details of Advogato's 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. Findability and Exploration: the future of search The majority of people visiting a news website don’t care about the front page. They might have reached your site from Google while searching for a very specific topic. They might just be wandering around. Or they’re visiting your site because they’re interested in one specific event that you cover. This is big. It changes the way we should think about news websites.

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. 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?

Design Patterns: Faceted Navigation We are pleased to present an excerpt from Chapter 4 of Search Patterns by Peter Morville and Jeffery Callender (O’Reilly, 2010). —Ed. Faceted Navigation#section1 Also called guided navigation and faceted search, the faceted navigation model leverages metadata fields and values to provide users with visible options for clarifying and refining queries. Faceted navigation is arguably the most significant search innovation of the past decade.[2] It features an integrated, incremental search and browse experience that lets users begin with a classic keyword search and then scan a list of results. It also serves up a custom map (usually to the left of results) that provides insights into the content and its organization and offers a variety of useful next steps.

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. 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. Listen to what they are saying and how they are saying it and follow their lead.

Testing Search for Relevancy and Precision Despite the fact that site search often receives the most traffic, it’s also the place where the user experience designer bears the least influence. Few tools exist to appraise the quality of the search experience, much less strategize ways to improve it. When it comes to site search, user experience designers are often sidelined like the single person at an old flame’s wedding: Everything seems to be moving along without you, and if you slipped out halfway through, chances are no one would notice. Kleiner-Backed Lockerz Acquires Social Sharing Platform AddToAny Exclusive-Social commerce network Lockerz has acquired 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.

Related: