Home
Roger Chen's Library tagged → View Popular
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.
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.
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.
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.
Evaluating Recommender Systems - Microsoft Research
Microsoft Research Tech Report (MSR-TR-2009-159)
CEUR-WS.org/Vol-532 - Recommender Systems and the Social Web 2009
Proceedings of the Workshop on Recommender Systems and the Social Web,
collocated with the 3rd ACM Conference on Recommender Systems (RecSys'09),
TechnoCalifornia: The Wisdom of the Few
One of the most common approaches to Recommender Systems is the so-called Collaborative Filtering. The main rationale is the following: In order to predict items that you will like, we find the most similar users to you by looking at your previous likes and dislikes. We then recommend items that those users have liked, but you still don't know.
Whimsley: Netflix Prize: Was The Napoleon Dynamite Problem Solved?
-
people aren't lists of numbers and don't watch movies as if they were
-
Anchoring suggests that rating systems need to take account of inertia — a user who has recently given a lot of above-average ratings is likely to continue to do so.
- 7 more annotations...
Selected Tags
Related Tags
Sponsored Links
Top Contributors
Groups interested in recommen...
Highlighter, Sticky notes, Tagging, Groups and Network: integrated suite dramatically boosting research productivity. Learn more »
Join Diigo