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Roger Chen

Roger Chen's Public Library

24 Nov 09

Trust network datasets - TrustLet

a free, collaborative project for collecting and analyzing information about trust metrics.

www.trustlet.org/...Trust_network_datasets - Preview

dataset data mining research

SVDLIBC

SVDLIBC is a C library based on the SVDPACKC library, which was written by Michael Berry, Theresa Do, Gavin O'Brien, Vijay Krishna and Sowmini Varadhan. SVDLIBC offers a cleaned-up version of the code with a sane library interface and a front-end executable that performs matrix file type conversions, along with computing singular value decompositions. Currently the only SVDPACKC algorithm implemented in SVDLIBC is las2, because it seems to be consistently the fastest. This algorithm has the drawback that the low order singular values may be relatively imprecise, but that is not a problem for most users who only want the higher-order values or who can tolerate some imprecision.

tedlab.mit.edu/svdlibc - Preview

recommender programming

Marius Muja - Home Page : FLANN - FLANN browse

FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset.

people.cs.ubc.ca/...FLANN - Preview

python recommender programming

Divisi: Commonsense Reasoning over Semantic Networks

Divisi uses a sparse higher-order SVD can help find related concepts, features, and relation types in any knowledge base that can be represented as a semantic network. By including common sense knowledge from ConceptNet, the results can include relationships not expressed in the original data but related by common sense.

divisi.media.mit.edu - Preview

python recommender programming

Recommender Systems for Social Bookmarking

In this thesis, we investigate how recommender systems can be applied to the domain of social bookmarking. More specifically, we want to investigate the task of item recommendation. For this purpose, interesting and relevant items---bookmarks or scientific articles---are retrieved and recommended to the user. Recommendations can be based on a variety of information sources about the user and the items. It is a difficult task as we are trying to predict which items out of a very large pool would be relevant given a user's interests, as represented by the items which the user has added in the past. In our experiments we distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. We are among the first to investigate this content-based aspect of recommendation for social bookmarking websites. We compare and combine the content-based aspect with the more common usage-based approaches.

ilk.uvt.nl/phd-thesis - Preview

research papers recommender

PMML - AnalyticBridge

PMML (Predictive Model Markup Language) provides a standard way to represent data mining models so that these can be shared between different statistical applications.

www.analyticbridge.com/pmml - Preview

data mining reference

rpy2 － redesign of rpy

rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions.

rpy.sourceforge.net/rpy2.html - Preview

python statistics tools data mining programming

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