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Tag recommendations based on tensor dimensionality reduction (2008)
| Venue: | In RecSys ’08: Proc. of the ACM Conference on Recommender systems, 43–50 |
| Citations: | 54 - 1 self |
BibTeX
@INPROCEEDINGS{Symeonidis08tagrecommendations,
author = {Panagiotis Symeonidis and Alexandros Nanopoulos and Yannis Manolopoulos},
title = {Tag recommendations based on tensor dimensionality reduction},
booktitle = {In RecSys ’08: Proc. of the ACM Conference on Recommender systems, 43–50},
year = {2008}
}
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Abstract
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 item and (ii) information items may have multiple facets. In contrast to the current tag recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items and tags. These data is represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. We perform experimental comparison of the proposed method against two state-of-the-art tag recommendations algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.
Keyphrases
tensor dimensionality reduction tag recommendation information item significant improvement current tag recommendation algorithm web link common consensus multiple facet latent semantic analysis unified framework social tagging many user state-of-the-art tag recommendation order singular value decomposition appropriate tag recommendation dimensionality reduction experimental comparison 3-order tensor real data set different interest social tagging system
