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Title:
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PARALLEL HIGHER-ORDER SVD FOR TAG-RECOMMENDATIONS |
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Author(s):
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Philipp Shah, Christoph Wieser, François Bry |
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ISBN:
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978-989-8533-09-8 |
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Editors:
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Bebo White and Pedro Isaías |
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Year:
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2012 |
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Edition:
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Single |
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Keywords:
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Search, Ranking, parallel HOSVD, social tagging |
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Type:
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Full Paper |
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First Page:
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257 |
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Last Page:
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265 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Social Tagging has become a method of choice for enriching data (like pictures) with meta-data which in turn can be used for searching (like retrieving art pictures) or tag recommendations relying on Singular Value Decompositions (SVD) to reduce dimensionality. We observed that social tagging-based search or tag-recommendation is more successful, if several dimensions describing the tagging community (like age, interests, cultural background) are considered. In other words, social tagging-based search calls for higher-order, or tensor-based, SVD instead of standard, matrix-based, SVD. This article reports on a parallelization of higher-order SVD which achieves a significantly better time complexity by comparison to single processor approaches. This time complexity is possible thanks to a careful use of Hestenes' SVD. This article also reports on a first experimental evaluation of the approach for a tag recommendation system. |
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