Group items tagged

4 verschillende metrics worden behandeld; Link Analysis Ranking, Similarity Recommendation, Personalized Ranking, Contextual Recommendation.

The tools used to search and find Learning Objects in different systems do not provide a meaningful and scalable way to rank or recommend learning material. This work propose and detail the use of Contextual Attention Metadata, gathered from the different tools used in the lifecycle of the Learning Object, to create ranking and recommending metrics to improve the user experience. Four types of metrics are detailed: Link Analysis Ranking, Similarity Recommendation, Personalized Ranking and Contextual Recommendation. While designed for Learning Objects, it is shown that these metrics could also be applied to rank and recommend other types of reusable components like software libraries.

recommender systems, social networks, diversity ...

Online communities have become become a crucial ingredient of e-business. Supporting open social networks builds strong brands and provides lasting value to the consumer. One function of the community is to recommend new products and services. Open social networks tend to be resilient, adaptive, and broad, but simplistic recommender systems can be 'gamed' by members seeking to promote certain products or services. We argue that the gaming is not the failure of the open social network, but rather of the function used by the recommender. To increase the quality and resilience of recommender systems, and provide the user with genuine and novel discoveries, we have to foster diversity, instead of closing down the social networks. Fortunately, software increases the broadcast capacity of each individual, making dense open social networks possible. Numerically, we show that dense social networks encourage diversity. In business terms, dense social networks support a long tail.

User profiles can be used to identify persons inside a community with similar interests. Folksonomy systems allow users to individually tag the objects of a common set (e.g., web pages). In this paper, we propose to create user profiles from the data available in such folksonomy systems by letting users specify the most relevant objects in the system. Instead of using the objects directly to represent the user profile, we propose to use the tags associated with the specified objects to build the user profile. We have designed a prototype for the research domain to use such tag-based profiles in finding persons with similar interests. The combination of tag-based profiles with standard recommender system technology has resulted in a new kind of recommender system to recommend related publications, keywords, and persons.

user profiles based on tagging

article about social information filtering: items are recommended based upon values assigned by other people with similar taste.

Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. Eigentaste was patented by UC Berkeley in 2003. It has many possible applications, such as the recommendation of books, movies, toys, stocks, and music. It was originally used in an online joke recommendation system called Jester, which recommends new jokes to users based on their ratings of an initial set.

A Guide to Recommender Systems

Deze service is APML enabled: APML is een standaard voor het delen van activity profiles, oftewel "Attention Profiles".

Cluztr is a social network built around web clicks. Share your clicks with your friends, meet like-minded people and discover new content based on personalized recommendations.

gRSShopper is an application that allows you to define your own community of RSS feeds, aggregates content from those feeds and organizes it, and helps you integrate that content into your own posts, articles and other content. It is a research database, a blogging engine, a community website, a content management system, and ultimately, a personal learning environment. The software is written in a computer language called Perl and is loaded onto web servers. It uses a database to manage your links, posts and other content. You access it with your web browser.

erg interessant en doordacht systeem.

Letizia is a user interface agent that assists a user browsing the World Wide Web. As the user operates a conventional Web browser such as Netscape, the agent tracks user behavior and attempts to anticipate items of interest by doing concurrent, autonomous exploration of links from the user's current position. The agent automates a browsing strategy consisting of a best-first search augmented by heuristics inferring user interest from browsing behavior.

erg interessant paper over een recommendation technology ontwikkeld bij MIT in 95

another article about attention profiling

nog een artikel over attention metadata

interessant initiatief.

interesting Dutch startup trying to let blogs evolve into social networks..

This website is a proof of concept that a better Social Media can exist; a social media based on interests and "personal relevancy" instead of popularity. * Help you decide whether to follow someone or not * Help you find people who share your interests * Start or participate in disucssions about your interests