Tag recommendations based on tensor dimensionality reduction

@inproceedings{Symeonidis2008TagRB,
  title={Tag recommendations based on tensor dimensionality reduction},
  author={P. Symeonidis and A. Nanopoulos and Y. Manolopoulos},
  booktitle={RecSys '08},
  year={2008}
}
Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming to develop a common consensus about which tags best describe an item. However, they fail to provide appropriate tag recommendations, because: (i) users may have different interests for an information… Expand
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References

SHOWING 1-10 OF 24 REFERENCES
Towards the Semantic Web: Collaborative Tag Suggestions
TLDR
This work defines a set of general criteria for a good tagging system and proposes a collaborative tag suggestion algorithm using these criteria to spot high-quality tags and employs a goodness measure for tags derived from collective user authorities to combat spam. Expand
Tag Recommendations in Folksonomies
TLDR
This paper evaluates and compares two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank, showing that both provide better results than non-personalized baseline methods. Expand
CubeSVD: a novel approach to personalized Web search
TLDR
Experimental evaluations using a real-world data set collected from an MSN search engine show that CubeSVD achieves encouraging search results in comparison with some standard methods. Expand
Tag-based social interest discovery
TLDR
An Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics, and shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections. Expand
Evaluation of Item-Based Top-N Recommendation Algorithms
TLDR
The experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better. Expand
The Structure of Collaborative Tagging Systems
TLDR
A dynamical model of collaborative tagging is presented that predicts regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. Expand
Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering
TLDR
This article proposes to deal with the sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback to solve the problem of sparse transactional data. Expand
The complex dynamics of collaborative tagging
TLDR
A generative model of collaborative tagging is produced in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise and how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags. Expand
Cubic Analysis of Social Bookmarking for Personalized Recommendation
TLDR
A division algorithm and a CubeSVD algorithm are proposed to analysis this information got from social bookmarking websites, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Expand
An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms
TLDR
An analysis framework is applied that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component, and identifies the three components identified are similarity computation, neighbor selection, and rating combination. Expand
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