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49
Survey on Social Tagging Techniques
"... Social tagging on online portals has become a trend now. It has emerged as one of the best ways of associating metadata with web objects. With the increase in the kinds of web objects becoming available, collaborative tagging of such objects is also developing along new dimensions. This popularity h ..."
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Cited by 33 (0 self)
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Social tagging on online portals has become a trend now. It has emerged as one of the best ways of associating metadata with web objects. With the increase in the kinds of web objects becoming available, collaborative tagging of such objects is also developing along new dimensions. This popularity has led to a vast literature on social tagging. In this survey paper, we would like to summarize different techniques employed to study various aspects of tagging. Broadly, we would discuss about properties of tag streams, tagging models, tag semantics, generating recommendations using tags, visualizations of tags, applications of tags and problems associated with tagging usage. We would discuss topics like why people tag, what influences the choice of tags, how to model the tagging process, kinds of tags, different power laws observed in tagging domain, how tags are created, how to choose the right tags for recommendation, etc. We conclude with thoughts on future work in the area.
Exploring Social Tagging Graph for Web Object Classification
- In KDD’09
"... This paper studies web object classification problem with the novel exploration of social tags. Automatically classifying web objects into manageable semantic categories has long been a fundamental preprocess for indexing, browsing, searching, and mining these objects. The explosive growth of hetero ..."
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Cited by 28 (7 self)
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This paper studies web object classification problem with the novel exploration of social tags. Automatically classifying web objects into manageable semantic categories has long been a fundamental preprocess for indexing, browsing, searching, and mining these objects. The explosive growth of heterogeneous web objects, especially non-textual objects such as products, pictures, and videos, has made the problem of web classification increasingly challenging. Such objects often suffer from a lack of easy-extractable features with semantic information, interconnections between each
Gossiping personalized queries
- EDBT, volume 426 of ACM International Conference Proceeding Series
, 2010
"... This paper presents P3Q, a fully decentralized gossip-based proto-col to personalize query processing in social tagging systems. P3Q dynamically associates each user with social acquaintances shar-ing similar tagging behaviours. Queries are gossiped among such acquaintances, computed on the fly in a ..."
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Cited by 14 (4 self)
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This paper presents P3Q, a fully decentralized gossip-based proto-col to personalize query processing in social tagging systems. P3Q dynamically associates each user with social acquaintances shar-ing similar tagging behaviours. Queries are gossiped among such acquaintances, computed on the fly in a collaborative, yet parti-tioned manner, and results are iteratively refined and returned to the querier. Analytical and experimental evaluations convey the scalability of P3Q for top-k query processing. More specifically, we show that on a 10,000-user delicious trace, with little storage at each user, the queries are accurately computed within reasonable time and bandwidth consumption. We also report on the inherent ability of P3Q to cope with users updating profiles and departing. 1.
eXO: Decentralized autonomous scalable social networking
- In Proc. CIDR
, 2011
"... Social networks have been receiving increasingly greater attention. As they stand now, users are required to upload their content to make it available to others. Typically, this involves releasing ownership and control. As a result, battles have begun regarding the ownership, exploitation, and contr ..."
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Cited by 13 (1 self)
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Social networks have been receiving increasingly greater attention. As they stand now, users are required to upload their content to make it available to others. Typically, this involves releasing ownership and control. As a result, battles have begun regarding the ownership, exploitation, and control of content between users and social network owners. Further, given the rates of growth witnessed recently, questions about the scalability of the social network services are being raised. Therefore, the following questions naturally emerge: Is it possible to architect, design, and implement decentralized social networking services that ensure scalability and efficiency, while, respecting users ’ control of their content? Can this be achieved while content can be available to others for viewing and commenting, and permit key social networking activities like tagging? This paper presents eXO, a completely decentralized, scalable system that offers fundamental social networking services. We describe the architecture of eXO and its key components which encompass (i) techniques for content indexing, (ii) novel algorithms for ranked retrieval, (iii) appropriate similarity definitions that consist of a ’content ’ and a ’social ’ part, (iv) novel algorithms for efficient distributed content retrieval, (v) novel techniques that efficiently and scalably facilitate tagging and exploit tags to enrich query results, and (vi) novel scalable methods which permit the creation of personal networks, which allow for more “intimate ” sharing and associations between users. We report on our evaluation which showcases its efficiency and scalability characteristics. eXO source code will be freely available to all, to use and test.
Social Wisdom for Search and Recommendation
, 2008
"... Social-tagging communities offer great potential for smart recommendation and “socially enhanced ” searchresult ranking. Beyond traditional forms of collaborative recommendation that are based on the item-user matrix of the entire community, a specific opportunity of social communities is to reflect ..."
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Cited by 10 (1 self)
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Social-tagging communities offer great potential for smart recommendation and “socially enhanced ” searchresult ranking. Beyond traditional forms of collaborative recommendation that are based on the item-user matrix of the entire community, a specific opportunity of social communities is to reflect the different degrees of friendships and mutual trust, in addition to the behavioral similarities among users. This paper presents a framework for harnessing such social relations for search and recommendation. The framework is implemented in the SENSE prototype system, and its usefulness is demonstrated in experiments with an excerpt of the librarything community data.
Graph-based term weighting for information retrieval
, 2012
"... 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. Give ..."
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Cited by 8 (1 self)
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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
Taagle: Efficient, personalized search in collaborative tagging networks
- In Procedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD
, 2012
"... We demonstrate the Taagle system for top-k retrieval in social tag-ging systems (also known as folksonomies). The general setting is the following: users form a weighted social network, which may reflect friendship, similarity, or trust; items from a public pool of items (e.g., URLs, blogs, photos, ..."
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Cited by 7 (2 self)
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We demonstrate the Taagle system for top-k retrieval in social tag-ging systems (also known as folksonomies). The general setting is the following: users form a weighted social network, which may reflect friendship, similarity, or trust; items from a public pool of items (e.g., URLs, blogs, photos, documents) are tagged by users with keywords; users search for the top-k items having certain tags. Going beyond a classic search paradigm where data is decoupled from the users querying it, users can now act both as producers and seekers of information. Hence finding the most relevant items in response to a query should be done in a network-aware manner: items tagged by users who are closer (more similar) to the seeker should be given more weight than items tagged by distant users. We illustrate with Taagle novel algorithms and a general ap-proach that has the potential to scale to current applications, in an online context where the social network, the tagging data and even the seekers ’ search ingredients can change at any moment. We also illustrate possible design choices for providing users a fully-personalized and customizable search interface. By this interface, they can calibrate how social proximity is computed (for example, with respect to similarity in tagging actions), how much weight the social score of tagging actions should have in the result build-up, or the criteria by which the user network should be explored. In order to further reduce running time, seekers are given the possibility to chose between exact or approximate answers, and can benefit from cached results of previous queries (materialized views).
Diversified top-k graph pattern matching
- PVLDB
"... Graph pattern matching has been widely used in e.g., so-cial data analysis. A number of matching algorithms have been developed that, given a graph pattern Q and a graph G, compute the set M(Q;G) of matches of Q in G. How-ever, these algorithms often return an excessive number of matches, and are ex ..."
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Cited by 7 (1 self)
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Graph pattern matching has been widely used in e.g., so-cial data analysis. A number of matching algorithms have been developed that, given a graph pattern Q and a graph G, compute the set M(Q;G) of matches of Q in G. How-ever, these algorithms often return an excessive number of matches, and are expensive on large real-life social graphs. Moreover, in practice many social queries are to find matches of a specific pattern node, rather than the entire M(Q;G). This paper studies top-k graph pattern matching. (1) We revise graph pattern matching defined in terms of simula-tion, by supporting a designated output node uo. Given G and Q, it is to find those nodes in M(Q;G) that match uo, instead of the large setM(Q;G). (2) We study two classes of functions for ranking the matches: relevance functions r() based on, e.g., social impact, and distance functions d() to cover diverse elements. (3) We develop two algorithms for computing top-k matches of uo based on r(), with the early termination property, i.e., they find top-k matches without computing the entireM(Q;G). (4) We also study diversified top-k matching, a bi-criteria optimization problem based on both r() and d(). We show that its decision problem is NP-complete. Nonetheless, we provide an approximation algorithm with performance guarantees and a heuristic one with the early termination property. (5) Using real-life and synthetic data, we experimentally verify that our (diversi-fied) top-k matching algorithms are effective, and outper-form traditional matching algorithms in efficiency. 1.
Network-aware search in social tagging applications: instance optimality versus efficiency
- in CIKM
, 2013
"... We consider in this paper top-k query answering in social applica-tions, with a focus on social tagging. This problem requires a sig-nificant departure from socially agnostic techniques. In a network-aware context, one can (and should) exploit the social links, which can indicate how users relate to ..."
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Cited by 5 (2 self)
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We consider in this paper top-k query answering in social applica-tions, with a focus on social tagging. This problem requires a sig-nificant departure from socially agnostic techniques. In a network-aware context, one can (and should) exploit the social links, which can indicate how users relate to the seeker and how much weight their tagging actions should have in the result build-up. We propose algorithms that have the potential to scale to current applications. While the problem has already been considered in previous lit-erature, this was done either under strong simplifying assumptions or under choices that cannot scale to even moderate-size real-world applications. We first revisit a key aspect of the problem, which is accessing the closest or most relevant users for a given seeker. We describe how this can be done on the fly (without any pre-computations) for several possible choices – arguably the most natural ones – of proximity computation in a user network. Based on this, our top-k algorithm is sound and complete, addressing the applicability issues of the existing ones. Moreover, it performs sig-nificantly better in general and is instance optimal in the case when the search relies exclusively on the social weight of tagging actions. To further address the efficiency needs of online applications, for which the exact search, albeit optimal, may still be expensive, we then consider approximate algorithms. Specifically, these rely on concise statistics about the social network or on approximate shortest-paths computations. Extensive experiments on real-world data from Twitter show that our techniques can drastically improve response time, without sacrificing precision.
Neighborhood-Based Tag Prediction
, 2009
"... We consider the problem of tag prediction in collaborative tagging systems where users share and annotate resources on the Web. We put forward HAMLET, a novel approach to automatically propagate tags along the edges of a graph which relates similar documents. We identify the core principles underly ..."
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Cited by 5 (0 self)
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We consider the problem of tag prediction in collaborative tagging systems where users share and annotate resources on the Web. We put forward HAMLET, a novel approach to automatically propagate tags along the edges of a graph which relates similar documents. We identify the core principles underlying tag propagation for which we derive suitable scoring models combined in one overall ranking formula. Leveraging these scores, we present an efficient top-k tag selection algorithm that infers additional tags by carefully inspecting neighbors in the document graph. Experiments using real-world data demonstrate the viability of our approach in large-scale environments where tags are scarce.
