FoldCons: A simple way to improve tag recommendation

Modou Gueye; Talel Abdessalem; Hubert Naacke

FoldCons: A simple way to improve tag recommendation

Modou Gueye
, Talel Abdessalem
, Hubert Naacke

Université Cheikh Anta DIOP
LIP6, UPMC Sorbonne Universités - Paris 6

Research output: Contribution to journal › Conference article › peer-review

Abstract

Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for tagging an item. In this paper we present a part of our work in progress which is a novel improvement of recommendations by re-ranking the output of a tag recommender. We mine association rules between candidates tags in order to determine a more consistent list of tags to recommend. Our method is an add-on one which leads to better recommendations as we show in this paper. It is easily parallelizable and morever it may be applied to a lot of tag recommenders. The experiments we did on five datasets with two kinds of tag recommender demonstrated the efficiency of our method.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	1066
Publication status	Published - 1 Jan 2013
Event	5th ACM RecSys Workshop on Recommender Systems and the Social Web, RSWeb 2013 - Co-located with the 7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China Duration: 13 Oct 2013 → …

Keywords

Association rules mining
Factorization models
Social network
Tag recommendation

Cite this

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title = "FoldCons: A simple way to improve tag recommendation",

abstract = "Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for tagging an item. In this paper we present a part of our work in progress which is a novel improvement of recommendations by re-ranking the output of a tag recommender. We mine association rules between candidates tags in order to determine a more consistent list of tags to recommend. Our method is an add-on one which leads to better recommendations as we show in this paper. It is easily parallelizable and morever it may be applied to a lot of tag recommenders. The experiments we did on five datasets with two kinds of tag recommender demonstrated the efficiency of our method.",

keywords = "Association rules mining, Factorization models, Social network, Tag recommendation",

author = "Modou Gueye and Talel Abdessalem and Hubert Naacke",

year = "2013",

month = jan,

day = "1",

language = "English",

volume = "1066",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

note = "5th ACM RecSys Workshop on Recommender Systems and the Social Web, RSWeb 2013 - Co-located with the 7th ACM Conference on Recommender Systems, RecSys 2013 ; Conference date: 13-10-2013",

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TY - JOUR

T1 - FoldCons

T2 - 5th ACM RecSys Workshop on Recommender Systems and the Social Web, RSWeb 2013 - Co-located with the 7th ACM Conference on Recommender Systems, RecSys 2013

AU - Gueye, Modou

AU - Abdessalem, Talel

AU - Naacke, Hubert

PY - 2013/1/1

Y1 - 2013/1/1

N2 - Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for tagging an item. In this paper we present a part of our work in progress which is a novel improvement of recommendations by re-ranking the output of a tag recommender. We mine association rules between candidates tags in order to determine a more consistent list of tags to recommend. Our method is an add-on one which leads to better recommendations as we show in this paper. It is easily parallelizable and morever it may be applied to a lot of tag recommenders. The experiments we did on five datasets with two kinds of tag recommender demonstrated the efficiency of our method.

AB - Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for tagging an item. In this paper we present a part of our work in progress which is a novel improvement of recommendations by re-ranking the output of a tag recommender. We mine association rules between candidates tags in order to determine a more consistent list of tags to recommend. Our method is an add-on one which leads to better recommendations as we show in this paper. It is easily parallelizable and morever it may be applied to a lot of tag recommenders. The experiments we did on five datasets with two kinds of tag recommender demonstrated the efficiency of our method.

KW - Association rules mining

KW - Factorization models

KW - Social network

KW - Tag recommendation

M3 - Conference article

AN - SCOPUS:84924370976

SN - 1613-0073

VL - 1066

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 13 October 2013

ER -

FoldCons: A simple way to improve tag recommendation

Abstract

Keywords

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