Social tag prediction

Heymann, Paul; Ramage, Daniel; García-Molina, Héctor

doi:10.1145/1390334.1390425

Cited by 235 publications

(157 citation statements)

References 12 publications

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“…Recently, some early steps have been taken in (Heymann, Ramage, & Garcia-Molina, 2008;Sigurbjörnsson & van Zwol, 2008;Song et al, 2008). Heymann et al (2008) studied del.icio.us, proposing an entropy-based metric for the tag suggestion task; Sigurbjörnsson and Van Zwol Sigurbjörnsson and van Zwol (2008) proposed tag suggestion for photos in flickr; Song et al (2008) looked into efficiency issues, proposing a fast tag recommendation method. Compared to our paper, these studies have implicitly removed the user from the relationship between tags and items.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Personalization of tagging systems

Wang

Clements

Yang

et al. 2010

Information Processing & Management

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a b s t r a c tSocial media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user's own preference and the opinion of others.

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Section: Discussion and Related Workmentioning

confidence: 99%

Personalization of tagging systems

Wang

Clements

Yang

et al. 2010

Information Processing & Management

View full text Add to dashboard Cite

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“…The underlying intuition remains on the fact that users with similar pro les might be interested in the same or similar documents/items. • Social IR (SIR) [3,6,15,65], based on the analysis of social networks by modeling interactions between users and leveraging social indicators related to documents and users to improve the estimation of the document relevance [49].…”

Section: The DI Erent Forms Of Collaborationmentioning

confidence: 99%

On the Collaboration Support in Information Retrieval

Soulier

Tamine

2017

ACM Comput. Surv.

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Collaborative Information Retrieval (CIR) is a well-known setting in which explicit collaboration occurs among a group of users working together to solve a shared information need. This type of collaboration has been deemed as bene cial for complex or exploratory search tasks. With the multiplicity of factors impacting on the search e ectiveness (e.g., collaborators' interactions or the individual perception of the shared information need), CIR gives rise to several challenges in terms of collaboration support through algorithmic approaches. More particularly, CIR should allow us to satisfy the shared information need by optimizing the collaboration within the search session over all collaborators, while ensuring that both mutually bene cial goals are reached and that the cognitive cost of the collaboration does not impact the search e ectiveness. In this survey, we propose an overview of CIR with a particular focus on the collaboration support through algorithmic approaches. The objective of this article is (a) to organize previous empirical studies analyzing collaborative search with the goal to provide useful design implications for CIR models, (b) to give a picture of the CIR area by distinguishing two main categories of models using the collaboration mediation axis, and (c) to point out potential perspectives in the domain.

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“…There havebeen a significant amount of work in predicting the tags fordocuments or other resources (webpages, images, videos) [1], [2], [3], [4].Depending on the object and theuser involvement, this approaches, have different assumptionson what is expected as an input;nevertheless,the goals are similar as theyexpect to find missing tags that arerelated with the object. We argue that our approach isdifferent as we use the workload to augment the documentvisibility after the tagging process.…”

Section: Related Work Collaborative Annotationmentioning

confidence: 99%

Assisting Text Annotation based on Attributes Suggestion Strategy using Content and Query

Palekar¹,

Dhok²,

Gawande³

2017

International Journal of Advanced Research in Computer and Comm

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In the current computing world, machine based data innovations have been broadly used to help numerous associations, individual businesses, scholarly and training establishments to deal with their procedures and data frameworks. Data frameworks are utilized to keep an eye on information. A general information administration framework that is prepared to do dealing with a few sorts of information, data stored in the database is known as Database Management System (DBMS). Accumulations of enormous, extensive text based Information contain huge measure of organized data, which remains unstructured content. Important data is constantly hard to find in these documents. In this paper we proposed a novel approach that encourages the era of the organized metadata by recognizing records that are prone to contain data of investment and this data is going to be helpful for questioning the database. Here individuals will lying on your front to allot metadata identified with records which they transfer which will effortlessly help the clients in recovering the records.

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Social tag prediction

Cited by 235 publications

References 12 publications

Personalization of tagging systems

Personalization of tagging systems

On the Collaboration Support in Information Retrieval

Assisting Text Annotation based on Attributes Suggestion Strategy using Content and Query

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