An Augmented User Model for Personalized Search in Collaborative Social Tagging Systems

Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation can simplify and streamline the user experience, and by modeling user preferences, predictive accuracy can be significantly improved. However, previous methods typically model user behavior based only on a log of prior tags, neglecting other behaviors and information in social tagging systems, e.g., commenting on items and connecting with other users. On the other hand, little is known about the connection and correlations among these behaviors and contexts in social tagging systems.In this paper, we investigate improved modeling for predictive social tagging systems. Our explanatory analyses demonstrate three significant challenges: coupled high order interaction, data sparsity and cold start on items. We tackle these problems by using a generalized latent factor model and fully Bayesian treatment. To evaluate performance, we test on two real-world data sets from Flickr and Bibsonomy. Our experiments on these data sets show that to achieve best predictive performance, it is necessary to employ a fully Bayesian treatment in modeling high order relations in social tagging system. Our methods noticeably outperform state-of-the-art approaches.

An ever increasing number of Web sites that allow users to easily collaborate, produce, and share content and interact have turned the Web into a more dynamic social place often referred to as Social Web. One form of Social Web sites is collaborative tagging systems that allow their users to annotate Web resources using tags, thus intuitively organizing them and making them easily findable. Tags would have been much more useful if collaborative tagging systems had been collaborating and allowing for integration of their tagging metadata. In this paper we address this issue of lack of collaboration by suggesting an approach for achieving that collaboration. We also present a concrete system called TagFusion that we developed to test the feasibility of the suggested approach. Special attention has been given to the strategies for attracting collaborative tagging systems to integrate using TagFusion, as well as to the possibility of involving artificial entities in the annotation process in accordance with the Semantic Web vision.

With the prosperity and popularity of social tagging communities, developing an enormous amount of user-generated data with modalities has emerged in recent years. Personalized search based on tag-based profiles, an indispensable and prominent way to assist users to access and retrieve their interested resources, has been extensively studied by research communities. In this paper, we revisit the extant profiling approaches to personalized search in collaborative tagging systems. Specifically, we attempt to answer the following research questions: (i) how many tags are sufficient for user and resource profiling? and (ii) under what circumstances should profile enriching/refining techniques be used to promote the effectiveness of personalized search? The result of our experimental studies in a real-world dataset indicate that the rational size of tags for constructing user/resource profiles does exist. This size can also provide us with an insight into when profiles should be enriched or refined for active/inactive users and resources. We believe the findings of this paper can be quite useful for the future applications in social tagging systems such as user interest predictions or resource recommendations.

Collaborative tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. On the other hand, numerous content-based profiling techniques have been developed to address the problem of obtaining accurate models of user information preferences in order to assist users with information-related tasks such as Web browsing or searching. In this paper we propose a hybrid user profiling strategy that takes advantage of both content-based profiles describing long-term information interests that a recommender system can acquired along time and interests revealed through tagging activities, with the goal of enhancing the interaction of users with a collaborative tagging system. Experimental results of using hybrid profiles for tag recommendation are reported and possible applications of these profiles for obtaining personalized recommendations in collaborative tagging systems are discussed.

Considering the natural tendency of people to follow direct or indirect cues of other people's activities, collaborative filtering-based recommender systems often predict the utility of an item for a particular user according to previous ratings by other similar users. Consequently, effective searching for the most related neighbors is critical for the success of the recommendations. In recent years, collaborative tagging systems with social bookmarking as their key component from the suite of Web 2.0 technologies allow users to freely bookmark and assign semantic descriptions to various shared resources on the web. While the list of favorite web pages indicates the interests or taste of each user, the assigned tags can further provide useful hints about what a user thinks of the pages.In this paper, we propose a new collaborative filtering approach TBCF (Tag-based Collaborative Filtering) based on the semantic distance among tags assigned by different users to improve the effectiveness of neighbor selection. That is, two users could be considered similar not only if they rated the items similarly, but also if they have similar cognitions over these items. We tested TBCF on real-life datasets, and the experimental results show that our approach has significant improvement against the traditional cosine-based recommendation method while leveraging user input not explicitly targeting the recommendation system.

PurposeThe purpose of this paper is to investigate the linking of a folksonomy (user vocabulary) and LCSH (controlled vocabulary) on the basis of word matching, for the potential use of LCSH in bringing order to folksonomies.Design/methodology/approachA selected sample of a folksonomy from a popular collaborative tagging system, Delicious, was word‐matched with LCSH. LCSH was transformed into a tree structure called an LCSH tree for the matching. A close examination was conducted on the characteristics of folksonomies, the overlap of folksonomies with LCSH, and the distribution of folksonomies over the LCSH tree.FindingsThe experimental results showed that the total proportion of tags being matched with LC subject headings constituted approximately two‐thirds of all tags involved, with an additional 10 percent of the remaining tags having potential matches. A number of barriers for the linking as well as two areas in need of improving the matching are identified and described. Three important tag distribution patterns over the LCSH tree were identified and supported: skewedness, multifacet, and Zipfian‐pattern.Research limitations/implicationsThe results of the study can be adopted for the development of innovative methods of mapping between folksonomy and LCSH, which directly contributes to effective access and retrieval of tagged web resources and to the integration of multiple information repositories based on the two vocabularies.Practical implicationsThe linking of controlled vocabularies can be applicable to enhance information retrieval capability within collaborative tagging systems as well as across various tagging system information depositories and bibliographic databases.Originality/valueThis is among frontier works that examines the potential of linking a folksonomy, extracted from a collaborative tagging system, to an authority‐maintained subject heading system. It provides exploratory data to support further advanced mapping methods for linking the two vocabularies.

Collaborative Tagging is the process of managing, classifying and describing tags where in user can easily describe or get an information about an item. As growth of information in social networking websites there is a possibility of increase of tags and it is very difficult to predict or recommend items to the user. In this paper we address the problem of sensitivity in the distribution of the tag data in clusters and optimization can be achieved by merging similar clusters in E-FCM and E-GK algorithm for collaborative Tagging Systems. Our experiments on each movie data set shows E-FCM and E-GK with sensitivity gives more accurate results as compared to baseline clustering approaches in collaborative tagging systems.

Collaborative tagging is a popular method in social resource sharing system. The reason why collaborative tagging system becomes popular is users can freely tags on the resources, like Flickr1 and Delicious2 . Any relationship between tags and resources are not made explicitly when tagging in folksonomy. Due to lack of semantic meaning, it is difficult for users to find related resources. Also, short of sufficient number of relevant tags increase the data sparsity which decreases the rate of information extraction against the user queries. Defining semantic relationship between tags, resources and users is an important research issue to retrieval of related information from folksonomies. In this paper, we propose a WordNet based method to find the semantic relationship between tags in folksonomy. Finally, the experiment results using Flickr data show our proposed method works well finding semantic relationship between tags in folksonomy.

Ontologies define a set of terms and the relationships (e.g., is-a and has-a) between them; they are the building block of the emerging semantic web. An ontology relating the tags in a collaborative tagging system (CTS) makes the CTS easier to understand. We propose an algorithm to automatically construct an ontology from CTS data and conduct a detailed empirical comparison with previous related work on four real data sets – Del.icio.us, LibraryThing, CiteULike, and IMDb. We also verify the effectiveness of our algorithm in detecting is-a and has-a relationships.Keywordsontologytaxonomytagcollaborative tagging systems

The rapidly growing social data created by users through Web 2.0 applications has intrigued active research in data mining and information retrieval (IR) community. The social tags created by users through social tagging system are one type of such social data. This thesis is dedicated to investigating whether and how the social tagging data can be utilized to enhance the performance of web mining and search methods. First, in order to reveal whether social tags are effective document features which can be used to represent and index web documents, the author compares social tags with other type of index terms, including expert-created subject terms, author-provided keywords and description terms, as well as the content words of web documents. The results of the comparison studies show that social tags contain both high-quality index terms and subjective and personal terms. Besides, like author-provided keywords and description terms, social tags provide additional information beyond the content words of tagged web documents, but social tags are more effective than author-provided keywords and description terms as independent document features for web clustering. The author further researches different approaches to improve web clustering performance by leveraging the social tagging data. The author proposes a novel clustering method called Tripartite Clustering which clusters web documents, users and tags simultaneously based on the social tagging network. The author also investigates two other social tagging-based clustering approaches with K-means and Link K-means clustering methods. Experimental results show that all tag-based clustering methods can significantly improve the performance of content-based clustering. Compared to tag-based K-means and Link K-means, Tripartite Clustering achieves equivalent or better performance and produces more useful information. The author also develops a novel personalized search framework based on a hyper-graph model of social tagging. During the search process, the proposed framework combines three types of relations from the social tagging network for query expansion and ranking: the social relation among users, the semantic relation among tags, and the tripartite relation among users, tags and web documents. Experiments demonstrate that the proposed personalized search framework is more efficient and effective than baseline search methods. Finally, the author proposes a novel topic model to simulate the generation of social tags and accordingly discover the topical structures of documents and users' tagging perspectives. Experimental analysis shows that the proposed Topic-Perspective model has better generative ability than topic models proposed in existing literature. Besides, this model also generates more useful information about document topics, user perspectives, and the impact of document topics and user perspectives on tag generation. In sum, this thesis not only reveals the potential value of social tags as document features and index terms of web documents, but also demonstrates how the social tagging network can be effectively utilized for web mining and search.

In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high‐quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph‐based algorithm, SPEAR (spamming‐resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext‐induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.

In recent years, the B2C e-commerce achieved a rapid development on a global scope; more and more people began to use the Internet for shopping. However, the exponentially increasing information provided by Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such problem is to build personalized recommender systems to retrieve product information that really interests the customers. The rapid development of Web 2.0 provides new ideas for personalized recommendation. In this paper we introduce the collaborative filtering, knowledge-based approaches and hybrid approaches in building recommender systems and discuss the strengths and weaknesses of each approach, we propose a collaborative tagging system to provide personalized product information to customers in B2C e-commerce websites and describe the system's architecture and point the system's advantage.

Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organizing their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improving annotation quality and reducing the cost of the annotation process. It has been also hypothesized that these methods should contribute to the consolidation of the vocabulary of collaborative tagging systems. However, to date, no empirical and quantitative result supports these hypotheses. In this work, we deeply analyze the impact of a tag recommendation system in the folksonomy of Freesound, a real-world and large-scale online sound sharing platform. Our results suggest that tag recommendation effectively increases vocabulary sharing among users of the platform. In addition, tag recommendation is shown to contribute to the convergence of the vocabulary as well as to a partial increase in the quality of annotations. However, according to our analysis, the cost of the annotation process does not seem to be effectively reduced. Our work is relevant to increase our understanding about the nature of tag recommendation systems and points to future directions for the further development of those systems and their analysis.

PurposeThe purpose of this paper is to report on a comparative and analytical examination of ten social tagging systems' interfaces and their features and functionalities. The specific objective of the study was to examine the ways in which the user interfaces of social tagging systems encourage and provide users with features to assign, explore, browse and make use of tags during their interaction with social tagging sites.Design/methodology/approachThe user interface features and functionalities of ten social tagging sites (six social bookmarking and four social media sharing sites) are examined. A categorisation of tag‐related features is developed for analysis. The sites are selected based on such criteria as popularity, variety of site type, and inclusion of tagging features and content type.FindingsThe findings of this study show that there is an emerging interface design paradigm with respect to social tagging sites that reflects a particular focus on exploratory search and browsing features and services. Some of the key areas discussed are: user tagging features; exploratory and tag browsing features; and interface layout.Practical implicationsThe findings of this study of the user interface features of social tagging sites provide a comprehensive picture of the possible and potential features that can be incorporated into new social tagging systems. Based on the evidence found in the examined social tagging interfaces, recommendations are made on the design of tag posting, tag use, tag browsing, tag lists and tag clouds. The design recommendations offer ideas for the development of more sophisticated exploratory and interactive user interfaces for social tagging systems.Originality/valueThis is the first paper that reports on a comparative and exploratory examination of social tagging user interface features and functionalities.

This paper analyzes the tagging patterns exhibited by users of del.icio.us, to assess how collaborative tagging supports and enhances traditional ways of classifying and indexing documents. Using frequency data and co‐word analysis matrices analyzed by multi‐dimensional scaling, the authors discovered that tagging practices to some extent work in ways that are continuous with conventional indexing. Small numbers of tags tend to emerge by unspoken consensus, and inconsistencies follow several predictable patterns that can easily be anticipated. However, the tags also indicated intriguing practices relating to time and task which suggest the presence of an extra dimension in classification and organization, a dimension which conventional systems are unable to facilitate.