Social Tagging Systems Research Articles

USER RECOMMENDATION ALGORITHM IN SOCIAL TAGGING SYSTEM BASED ON HYBRID USER TRUST

With the rapid growth of web 2.0 technologies, tagging become much more important today to facilitate personal organization and also provide a possibility for users to search information or discover new things with Collaborative Tagging Systems. However, the simplistic and user-centered design of this kind of systems cause the task of finding personally interesting users is becoming quite out of reach for the common user. Collaborative Filtering (CF) seems to be the most popular technique in recommender systems to deal with information overload issue but CF suffers from accuracy limitation. This is because CF always been at-tack by malicious users that will make it suffers in finding the truly interesting users. With this problem in mind, this study proposes a hybrid User Trust method to enhance CF in order to increase accuracy of user recommendation in social tagging system. This method is a combination of developing trust network based on user interest similarity and trust network from social network analysis. The user interest similarity is de-rived from personalized user tagging information. The hybrid User Trust method is able to find the most trusted users and selected as neighbours to generate recommendations. Experimental results show that the hybrid method outperforms the traditional CF algorithm. In addition, it indicated that the hybrid method give more accurate recommendation than the existing CF based on user trust.

Overview on Construction of Ontology in Social Tagging System

Overview on Construction of Ontology in Social Tagging System

The TFC Model: Tensor Factorization and Tag Clustering for Item Recommendation in Social Tagging Systems

In this paper, a novel Tensor Factorization and tag Clustering (TFC) model is presented for item recommendation in social tagging systems. The TFC model consists of three distinctive steps, in each of which important innovative elements are proposed. More specifically, through its first step, the content information is exploited to propagate tags between conceptual similar items based on a relevance feedback mechanism, in order to solve sparsity and “cold start” problems. Through its second step, sparsity is further handled, by generating tag clusters and revealing topics, following an innovative <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tf</i> · <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">idf</i> weighting scheme. Furthermore, we experimentally prove that a few number of expert tags can improve the performance of quality recommendations, since they contribute to more coherent tag clusters. Through its third step, the latent associations among users, topics, and items are revealed by exploiting the TF technique of high order singular value decomposition. This way the proposed TFC model tackles problems of real-world applications, which produce noise and decrease the quality of recommendations. In our experiments with real-world social data, we show that the proposed TFC model outperforms other state-of-the-art methods, which also exploit the TF technique of HOSVD.

Integrating ontology technology with folksonomies for personalized social tag recommendation

Social tagging systems leverage social interoperability by facilitating the searching, sharing, and exchanging of tagging resources. A major drawback of existing social tagging systems is that social tags are used as keywords in keyword-based search. They focus on keywords and human interpretability rather than on computer interpretable semantic knowledge. Therefore, social tags are useful for information sharing and organizing, but they lack the computer-interpretability needed to facilitate a personalized social tag recommendation. An interesting issue is how to automatically generate a personalized social tag recommendation list to users when a resource is accessed by users. The novel solution proposed in this study is a hybrid approach based on semantic tag-based resource profile and user preference to provide personalized social tag recommendation. Experiments show that the Precision and Recall of the proposed hybrid approach effectively improves the accuracy of social tag recommendation.

Folksonomia: a linguagem das tags

A radicalização do potencial colaborativo da web atual aponta uma tendência de personalização da recuperação da informação através de ferramentas que exploram a linguagem natural na representação e no compartilhamento de conteúdos ao longo das redes sociais. Tal configuração sócio-técnica traz desafios aos profissionais da informação tanto para a descrição e compreensão dos fenômenos informacionais que ocorrem neste âmbito, quanto para a elaboração de produtos e serviços voltados para um usuário que se apresenta cada vez mais como sujeito informacional ao assumir um papel ativo diante da complexidade que caracteriza a organização da informação em contextos digitais. Este artigo apresenta conclusões de pesquisa, relacionadas às analises da linguagem utilizada em três ambientes colaborativos que utilizam a folksonomia (Social Tagging Systems). A partir de uma perspectiva fundamentada na Semiótica e na Análise de Redes Sociais, são identificadas e descritas as principais manifestações da linguagem gerada e compartilhada pelas redes sociais através destes ambientes.

Extracting Usage Patterns and the Analysis of Tag Connection Dynamics within Collaborative Tagging Systems

Coll aborative tagging has become a very popular way of annotation, thanks to the fact that any entity may be labeled by any individual based on his own reason. In this paper we present the result s of the case study carried out on the basis of data gathered at different time intervals from the social tagging system developed and implemented on Intelepciune.ro. Analyzing collective data referring to the way in which community members associate different tags, we have observed that between tags, links are formed which become increasingly stable with the passing of time. Following the application of methodology specific to network analysis, we have managed to extract information referring to tag popularity, their influence within the network and the degree to which a tag depends upon another. As such, we have succeeded in determining different semantic structures within the collective tagging system and see their evolution at different stages in time. Furthermore, we have pictured the way in which tag recommendations can be executed and that they can be integrated within recommendation systems. Thus, we will be able to identify experts and trustworthy content based on different categories of interest.

Finding subject terms for classificatory metadata from user‐generated social tags

With the increasing popularity of social tagging systems, the potential for using social tags as a source of metadata is being explored. Social tagging systems can simplify the involvement of a large number of users and improve the metadata‐generation process. Current research is exploring social tagging systems as a mechanism to allow nonprofessional catalogers to participate in metadata generation. Because social tags are not from controlled vocabularies, there are issues that have to be addressed in finding quality terms to represent the content of a resource. This research explores ways to obtain a set of tags representing the resource from the tags provided by users. Two metrics are introduced. Annotation Dominance (AD) is a measure of the extent to which a tag term is agreed to by users. Cross Resources Annotation Discrimination (CRAD) is a measure of a tag's potential to classify a collection. It is designed to remove tags that are used too broadly or narrowly. Using the proposed measurements, the research selects important tags (meta‐terms) and removes meaningless ones (tag noise) from the tags provided by users. To evaluate the proposed approach to find classificatory metadata candidates, we rely on expert users' relevance judgments comparing suggested tag terms and expert metadata terms. The results suggest that processing of user tags using the two measurements successfully identifies the terms that represent the topic categories of web resource content. The suggested tag terms can be further examined in various usages as semantic metadata for the resources.

Emergence of fast agreement in an overhearing population: The case of the naming game

The naming game (NG) describes the agreement dynamics of a population of N agents interacting locally in pairs leading to the emergence of a shared vocabulary. This model has its relevance in the novel fields of semiotic dynamics and specifically to opinion formation and language evolution. The application of this model ranges from wireless sensor networks as spreading algorithms, leader election algorithms to user-based social tagging systems. In this paper, we introduce the concept of overhearing (i.e., at every time step of the game, a random set of Nδ individuals are chosen from the population who overhear the transmitted word from the speaker and accordingly reshape their inventories). When δ = 0 one recovers the behavior of the original NG. As one increases δ, the population of agents reaches a faster agreement with a significantly low-memory requirement. The convergence time to reach global consensus scales as logN as δ approaches 1.

Collaborative Filtering with User��s Interest and Grade in Social Tagging System

Collaborative Filtering with User��s Interest and Grade in Social Tagging System

BPR-UserRec: a personalized user recommendation method in social tagging systems

Social tagging is one of the most important characteristics of Web 2.0 services, and social tagging systems (STS) are becoming more and more popular for users to annotate, organize and share items on the Web. Moreover, online social network has been incorporated into social tagging systems. As more and more users tend to interact with real friends on the Web, personalized user recommendation service provided in social tagging systems is very appealing. In this paper, we propose a personalized user recommendation method, and our method handles not only the users' interest networks, but also the social network information. We empirically show that our method outperforms a state-of-the-art method on real dataset from Last.fm dataset and Douban.

Semantic Stability in Social Tagging Streams

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary users may never manage to reach a consensus on the description of the entities (e.g., books, user or songs) in the system. Yet, previous research has provided interesting evidence that the tag distributions of entities can become stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the stability of social tagging systems in a robust and methodical way? (ii) Does stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by making the following contributions: (i) we present a novel and robust method which overcomes a number of limitations in existing methods, (ii) we empirically investigate semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) we investigate potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language.

A self-adapted method for the categorization of social resources

Social tagging systems have become a popular system to organize information in many web 2.0 sites. They are also being rapidly adopted in enterprises to enhance information sharing, knowledge sharing and emerged as a novel categorization scheme based on the collective knowledge of people. Scalability is an issue of the categorization of the resources of social tagging systems. Scalability has highlighted a critical trade-off between accuracy and complexity. As social tagging systems evolve over time, resource categories can appear or disappear either by grouping new resources or disaggregating existing ones, and this implies the re-assignation of the resources involved to others categories. This makes the methods and/or algorithms that categorize resources of social tagging systems to be non-scalable, and then not efficiently implementable on real social tagging systems. This paper presents a simple method for categorizing resources on social tagging systems which is self-adaptive, scalable and implementable in any real social tagging system.

Topical community detection from mining user tagging behavior and interest

With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.

A novel user-based collaborative filtering method by inferring tag ratings

User-based collaborative filtering is one of the most widely-used recommendation methods. It recommends items to a user based on her similar users' preferences. The essential part of user-based collaborative filtering is to infer users' similarities. A common method is to compute the similarity between two users according to their ratings to co-rated items. In many cases, two users rate only few common items, such that the similarity between them is inaccurate and it results in misleading recommendations. With the boost of social tagging systems, exploiting social tag information has been a popular way to improve recommender systems in recent years. In this paper, we propose a novel method to compute users' similarities using inferred tag ratings. A user's preference for a tag t can be inferred upon her ratings of items tagged with t . In a case that a user rates few such items, then the inferred rating to t may be inaccurate. Hence the relationships among tags are taken into consideration to compute her preference for t according to her all item ratings, such that the inferred preference of the user could be more accurate. Evaluations were done on the MovieLens data set. The results indicate that our method can outperform the traditional user-based collaborative filtering.

Understanding information propagation on online social tagging systems: a case study on Flickr

Online social networking services (SNS) have been regarded as one of the most powerful online communication channels to propagate information to other users. It means that the online social networking services are providing users with efficient features (e.g., searching, managing and visualizing new information). It is important for many online collaborative applications to understand how the information can be propagated via such social media. Thus, we want to focus on a social tagging system (e.g., Flickr) where users can easily exchange resources as well as their tags. In this paper, given a certain tag, a social pulse can be established by counting (i) the number of users and (ii) the number of resources over time. More importantly, we assume that information can be propagated by (iii) inducibility from other tags by comparing social pulses. To conduct experimentation, a tag-based searching system (called Tagoole) has been implemented to collect a dataset from Flickr.

A knowledge-tracing model of learning from a social tagging system

We propose a user model to support personalized learning paths through online material. Our approach is a variant of student modeling using the computer tutoring concept of knowledge tracing. Knowledge tracing involves representing the knowledge required to master a domain, and, from traces of online user behavior, diagnosing user knowledge states as a profile over those elements. The user model is induced from documents tagged by an expert in a social tagging system. Tags identified with “expertise” in a domain can be used to identify a corpus of domain documents. That corpus can be fed to an automated process that distills a topic model representation characteristic of the domain. As a learner navigates and reads online material, inferences can be made about the degree to which topics in the target domain have been learned. We validate this knowledge tracing approach against data from a social tagging study. As part of this evaluation, we match the predictions of the knowledge-tracing model to individual participant responses made to individual question items used to test domain knowledge.

Understanding why users tag: A survey of tagging motivation literature and results from an empirical study

While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.

Evaluation of Folksonomy Induction Algorithms

Algorithms for constructing hierarchical structures from user-generated metadata have caught the interest of the academic community in recent years. In social tagging systems, the output of these algorithms is usually referred to as folksonomies (from folk-generated taxonomies). Evaluation of folksonomies and folksonomy induction algorithms is a challenging issue complicated by the lack of golden standards, lack of comprehensive methods and tools as well as a lack of research and empirical/simulation studies applying these methods. In this article, we report results from a broad comparative study of state-of-the-art folksonomy induction algorithms that we have applied and evaluated in the context of five social tagging systems. In addition to adopting semantic evaluation techniques, we present and adopt a new technique that can be used to evaluate the usefulness of folksonomies for navigation . Our work sheds new light on the properties and characteristics of state-of-the-art folksonomy induction algorithms and introduces a new pragmatic approach to folksonomy evaluation, while at the same time identifying some important limitations and challenges of folksonomy evaluation. Our results show that folksonomy induction algorithms specifically developed to capture intuitions of social tagging systems outperform traditional hierarchical clustering techniques. To the best of our knowledge, this work represents the largest and most comprehensive evaluation study of state-of-the-art folksonomy induction algorithms to date.

The collective knowledge of social tags: Direct and indirect influences on navigation, learning, and information processing

Tag clouds generated in social tagging systems can capture the collective knowledge of communities. Using as a basis spreading activation theories, information foraging theory, and the co-evolution model of cognitive and social systems, we present here a model for an extended information scent, which proposes that both collective and individual knowledge have a significant influence on link selection, incidental learning, and information processing. Two experimental studies tested the applicability of the model to a situation in which individual knowledge and collective knowledge were contradictory to each other. The results of the first experiment showed that a higher individual strength of association between a target in demand and a tag led to a higher probability of selecting corresponding links, combined with less thorough information processing for non-corresponding links. But users also adapted their navigation behavior to the collective knowledge (strength of associations of tags) of the community and showed incidental learning during navigation, which resulted in a change of their individual strength of associations. The second experiment confirmed these results and showed, in addition, that the effects also occurred for indirect associations. Altogether, the results show that the extended information scent is an appropriate and fertile model for describing the interplay of individual knowledge and the collective knowledge of social tags.

One‐class support vector machines for personalized tag‐based resource classification in social bookmarking systems

SUMMARYSocial tagging systems allow users to easily create, organize, and share collections of Web resources in a collaborative fashion. Videos, pictures, research papers, and Web pages are shared and annotated in sites such as Del.icio.us, CiteULike, or Flickr, among others. The rising popularity of these systems leads to a constant increase in the number of users actively publishing and annotating resources and, consequently, an exponential growth in the amount of data contained in their folksonomies, the underlying data structure of tagging systems. In turn, the user task of discovering interesting resources becomes more and more difficult and time‐consuming. In this paper, the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One‐class support vector machine classification is evaluated as a means to identify relevant information for users based exclusively on positive examples of their information preferences. It is assumed that users express their interest on resources belonging to a folksonomy by assigning tags to them, whereas there is no straightforward method to collect uninterestingness judgments. Filtering interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities of Web communities. In this paper, the results achieved with tag‐based classification are compared with those obtained using more traditional information sources such as the full text of Web pages. Experimental evaluation showed that tag‐based classifiers outperformed those learned using the text of documents as well as other content‐related sources. Moreover, tag‐based classification becomes essential for folksonomies in which no additional content is available because of the nature of resources being stored (e.g., tagging of photos or videos). Copyright © 2012 John Wiley &amp; Sons, Ltd.