Diversifying Tag Selection Result for Tag Clouds by Enhancing both Coverage and Dissimilarity

Wang, Meiling; Zhou, Xiang; Tao, Qiuming; Wu, Wei; Zhao, Chen

doi:10.1007/978-3-642-41154-0_3

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…The authors in [13] focused on user perspective and they proposed a probabilistic framework for solving the personalized tag recommendation, but without considering diversity. Result diversification has been studied in tag recommendation domain by [27,4]; however, they take into account the possible topics and their goal is to provide high coverage and low redundancy with respect to those topics. The authors in [4] used the general probabilistic framework in [1] to address relevance and coverage.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

The TagAdvisor

Nazi

Das

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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The increasing popularity and widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback (preferably positive) in order to mark their reputation and presence in the Web. Though a significant proportion of purchasing decisions today are driven by average numeric scores (e.g., movie rating in IMDB), detailed reviews are critical for activities such as buying an expensive digital SLR camera, reserving a vacation package, etc. Since writing a detailed review for a product (or, a service) is usually time-consuming and may not offer any incentive, the number of useful reviews available in the Web is far from many. The corpus of reviews available at our disposal for making informed decisions also suffers from spam and misleading content, typographical and grammatical errors, etc. In this paper, we address the problem of how to engage the lurkers (i.e., people who read reviews but never take time and effort to write one) to participate and write online reviews by systematically simplifying the reviewing task. Given a user and an item that she wants to review, the task is to identify the top-k meaningful phrases (i.e., tags) from the set of all tags (i.e., available user feedback for items) that, when advised, would help her review an item easily. We refer to it as the TagAdvisor problem, and formulate it as a generalconstrained optimization goal. Our framework is centered around three measures -relevance (i.e., how well the result set of tags describes an item to a user), coverage (i.e., how well the result set of tags covers the different aspects of an item), and polarity (i.e., how well sentiment is attached to the result set of tags) in order to help a user review an item satisfactorily. By adopting different definitions of coverage, we identify two concrete problem instances that enable a wide range of real-world scenarios. We show that these problems are NP-hard and develop practical algorithms with theoretical bounds to solve them efficiently. We conduct detailed experiments on synthetic and real data crawled from the web to validate the utility of our problem and effectiveness of our solutions.

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Section: Related Workmentioning

confidence: 99%

“…Tag Recommendation: Tag recommendation has been extensively studied in literature [4,10,25,13,27]. The authors in [13] focused on user perspective and they proposed a probabilistic framework for solving the personalized tag recommendation, but without considering diversity.…”

Section: Related Workmentioning

confidence: 99%

The TagAdvisor

Nazi

Das

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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“…Actually, the above social inÀuence maximization problem (viral marketing) is a variant of the set cover problem which has been well studied [34]. The reason is that this type of problems has broad applications, such as recommendation [35], ensemble pruning [36], tag selection [37], and document summary [38]. Though these problems are generally NP-hard, the optimization functions are usually monotone and submodular.…”

Section: Related Workmentioning

confidence: 99%

Social Marketing Meets Targeted Customers: A Typical User Selection and Coverage Perspective

Liu

Dong

Liu

et al. 2014

2014 IEEE International Conference on Data Mining

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The emergence of social networks has provided opportunities for both targeted marketing and viral marketing. By concentrating the efforts on a few key customers, targeted marketing could make the promotion of the items (products) much easier and more cost-effective. On the other hand, viral marketing aims at ¿nding a set of individuals (seeds) to maximize the word-of-mouth propagation of an item. However, these two marketing strategies can only exploit some speci¿c characteristics of the social networks, and the problem of how to combine them together to build a better, stronger business is still open. To that end, in this paper, we propose a general approach for integrated marketing. Speci¿cally, to market a given item, we ¿rst generate the item-speci¿c candidate users by a recommendation algorithm, and then select the typical users who have the best balanced utility scores and consumption/social entropy. Next, treating typical users as targeted customers, we study the problem of maximizing information awareness in viral marketing with these constrained targets. Along this line, we de¿ne it as a constrained coverage maximization problem, and propose three solutions: GMIC, LMIC and QMIC. Finally, extensive experimental results on real-world datasets demonstrate that our integrated marketing approach could outperform the methods that consider only targeted marketing or viral marketing.

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“…Tag Recommendation: Tag recommendation has been extensively studied in literature [Belém et al 2013;Feng and Wang 2012;Song et al 2008;Hu et al 2010;Wang et al 2013]. The authors in [Hu et al 2010] focused on user perspective and they proposed a probabilistic framework for solving the personalized tag recommendation, but without considering diversity.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in [Hu et al 2010] focused on user perspective and they proposed a probabilistic framework for solving the personalized tag recommendation, but without considering diversity. Result diversification has been studied in tag recommendation domain by [Wang et al 2013;Belém et al 2013]; however, they take into account the possible topics and their goal is to provide high coverage and low redundancy with respect to those topics. The authors in [Belém et al 2013] used the general probabilistic framework in [Agrawal et al 2009] to address relevance and coverage.…”

Section: Related Workmentioning

confidence: 99%

Web Item Reviewing Made Easy By Leveraging Available User Feedback

Nazi¹,

Das²,

Das³

2016

Preprint

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The increasing popularity and widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback (preferably positive) in order to mark their reputation and presence in the Web. Though a significant proportion of purchasing decisions today are driven by average numeric scores (e.g., movie rating in IMDB), detailed reviews are critical for activities such as buying an expensive digital SLR camera, reserving a vacation package, etc. Since writing a detailed review for a product (or, a service) is usually time-consuming and may not offer any incentive, the number of useful reviews available in the Web is far from many. The corpus of reviews available at our disposal for making informed decisions also suffers from spam and misleading content, typographical and grammatical errors, etc. In this paper, we address the problem of how to engage the lurkers (i.e., people who read reviews but never take time and effort to write one) to participate and write online reviews by systematically simplifying the reviewing task. Given a user and an item that she wants to review, the task is to identify the top-k meaningful phrases (i.e., tags) from the set of all tags (i.e., available user feedback for items) that, when advised, would help her review an item easily. We refer to it as the TagAdvisor problem, and formulate it as a general-constrained optimization goal. Our framework is centered around three measures -relevance (i.e., how well the result set of tags describes an item to a user), coverage (i.e., how well the result set of tags covers the different aspects of an item), and polarity (i.e., how well sentiment is attached to the result set of tags) in order to help a user review an item satisfactorily. By adopting different definitions of coverage, we identify two concrete problem instances that enable a wide range of real-world scenarios. We show that these problems are NP-hard and develop practical algorithms with theoretical bounds to solve them efficiently. We conduct detailed experiments on synthetic and real data crawled from the web to validate the utility of our problem and effectiveness of our solutions.

show abstract

Diversifying Tag Selection Result for Tag Clouds by Enhancing both Coverage and Dissimilarity

Cited by 4 publications

References 20 publications

The TagAdvisor

The TagAdvisor

Social Marketing Meets Targeted Customers: A Typical User Selection and Coverage Perspective

Web Item Reviewing Made Easy By Leveraging Available User Feedback

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