Efficient top-k querying over social-tagging networks

Schenkel, Ralf; Crecelius, Tom; Kacimi, Mouna; Michel, Sebastian; Neumann, Thomas; Parreira, Josiane Xavier; Weikum, Gerhard

doi:10.1145/1390334.1390424

Cited by 120 publications

(105 citation statements)

References 32 publications

(28 reference statements)

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“…Furthermore, an increasingly popular recent application of graph theoretic approaches to IR is in the context of social or collaborative networks and recommender systems (Craswell and Szummer 2007;Kleinberg 2006;Konstas et al 2009;Noh et al 2009;Schenkel et al 2008). …”

Section: Graphs In Information Retrievalmentioning

confidence: 99%

Graph-based term weighting for information retrieval

Blanco

Lioma²

2011

Inf Retrieval

159

140

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A standard approach to Information Retrieval (IR) is to model text as a bag of words. Alternatively, text can be modelled as a graph, whose vertices represent words, and whose edges represent relations between the words, defined on the basis of any meaningful statistical or linguistic relation. Given such a text graph, graph theoretic computations can be applied to measure various properties of the graph, and hence of the text. This work explores the usefulness of such graph-based text representations for IR. Specifically, we propose a principled graph-theoretic approach of (1) computing term weights and (2) integrating discourse aspects into retrieval. Given a text graph, whose vertices denote terms linked by co-occurrence and grammatical modification, we use graph ranking computations (e.g. PageRank Page et al. in The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998) to derive weights for each vertex, i.e. term weights, which we use to rank documents against queries. We reason that our graph-based term weights do not necessarily need to be normalised by document length (unlike existing term weights) because they are already scaled by their graph-ranking computation. This is a departure from existing IR ranking functions, and we experimentally show that it performs comparably to a tuned ranking baseline, such as BM25 (Robertson et al. in NIST Special Publication 500-236: TREC-4, 1995). In addition, we integrate into ranking graph properties, such as the average path length, or clustering coefficient, which represent different aspects of the topology of the graph, and by extension of the document represented as a graph. Integrating such properties into ranking allows us to consider issues such as discourse coherence, flow and density during retrieval. We experimentally show that this type of ranking performs comparably to BM25, and can even outperform it, across different TREC (Voorhees and Harman in TREC: Experiment and evaluation in information retrieval, MIT Press, 2005) datasets and evaluation measures.

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Section: Graphs In Information Retrievalmentioning

confidence: 99%

Graph-based term weighting for information retrieval

Blanco

Lioma²

2011

Inf Retrieval

159

140

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“…Previous studies have already shown that tags cannot only improve the search effectiveness (Heymann, Koutrika, & Garcia-Molina, 2008;Xu, Bao, Fei, Su, & Yu, 2008), but also support knowledge discovery (Li, Guo, & Zhao, 2008). Schenkel et al (2008) rank top-k results looking at social and semantic dimensions. Collaborative filtering predicts a user's interests by looking at other but similar users (user-based collaborative filtering, e.g., Herlocker et al, 1999;Zhang & Koren, 2007) or other but similar items to the target item (e.g., item-based collaborative filtering Deshpande & Karypis, 2004).…”

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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“…A social scoring function, leveraging the strength of user relations and correlations among different tags, was proposed in Schenkel et al [2008] to improve the top-k quality. Various notions of user affinities and social relations were also discussed in Amer-Yahia et al [2008b] and Schenkel et al [2008]. A general indexing and query processing framework, encompassing a wide class of scoring functions and networks, was developed in AmerYahia et al [2008a].…”

Section: Discussionmentioning

confidence: 99%

Collaborative personalized top-k processing

Bai

Guerraoui

Kermarrec

et al. 2011

ACM Trans. Database Syst.

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This article presents P4Q, a fully decentralized gossip-based protocol to personalize query processing in social tagging systems. P4Q dynamically associates each user with social acquaintances sharing similar tagging behaviors. Queries are gossiped among such acquaintances, computed on-the-fly in a collaborative, yet partitioned manner, and results are iteratively refined and returned to the querier. Analytical and experimental evaluations convey the scalability of P4Q for top-k query processing, as well its inherent ability to cope with users updating profiles and departing.

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Efficient top-k querying over social-tagging networks

Cited by 120 publications

References 32 publications

Graph-based term weighting for information retrieval

Graph-based term weighting for information retrieval

Personalization of tagging systems

Collaborative personalized top-k processing

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