Collaborative Tagging Systems Research Articles

Improving Collaborative Filtering Recommendations with Tag and Time Integration in Virtual Online Communities

In recent years, virtual online communities have experienced rapid growth. These communities enable individuals to share and manage images or websites by employing tags. A collaborative tagging system (CTS) facilitates the process by which internet users collectively organize resources. CTS offers a plethora of useful information, including tags and timestamps, which can be utilized for recommendations. A tag represents an implicit evaluation of the user’s preference for a particular resource, while timestamps indicate changes in the user’s interests over time. As the amount of information increases, it is feasible to integrate more detailed data, such as tags and timestamps, to improve the quality of personalized recommendations. The current study employs collaborative filtering (CF), which incorporates both tag and time information to enhance recommendation precision. A computational recommender system is established to generate weights and calculate similarities by incorporating tag data and time. The effectiveness of our recommendation model was evaluated by linearly merging tag and time data. In addition, the proposed CF method was validated by applying it to big data sets in the real world. To assess its performance, the size of the neighborhood was adjusted in accordance with the standard CF procedure. The experimental results indicate that our proposed method significantly improves the quality of recommendations compared to the basic CF approach.

Personalised resource recommendation method for collaborative tagging system based on machine learning

In order to overcome the low feasibility of traditional resource recommendation methods, this paper proposes a personalised resource recommendation method based on machine learning. Firstly, the user-based collaborative filtering algorithm is used to calculate user personalised similarity, and then content-based collaborative filtering algorithm is used to calculate resource content similarity through cosine similarity. Combined with user similarity and resource content similarity, a hybrid computing model of resource similarity is established, and personalised recommendation is realised through statistical machine learning. The experimental results show that: the F-measure value of the method can reach 0.97, the coverage rate is maintained above 50%, the popularity is above 0.8, and the MAE value is always the minimum, and its precision is always higher than that of the contrast method. It shows that the proposed method can effectively improve the precision and feasibility of personalised recommendation results.

A method of query expansion based on topic models and user profile for search in folksonomy

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

Tags, Borders, and Catalogs

Through a computational reading of the online book reviewing community LibraryThing, we examine the dynamics of a collaborative tagging system and learn how its users refine and redefine literary genres. LibraryThing tags are overlapping and multi-dimensional, created in a shared space by thousands of users, including readers, bookstore owners, and librarians. A common understanding of genre is that it relates to the content of books, but this resource allows us to view genre as an intersection of user communities and reader values and interests. We explore different methods of computational genre measurement within the open space of user-created tags. We measure overlap between books, tags, and users, and we also measure the homogeneity of communities associated with genre tags and correlate this homogeneity with reviewing behavior.Finally, by analyzing the text of reviews, we identify the thematic signatures of genres on LibraryThing, revealing similarities and differences between them. These measurements are intended to elucidate the genre conceptions of the users, not, as in prior work, to normalize the tags or enforce a hierarchy. We find that LibraryThing users make sense of genre through a variety of values and expectations, many of which fall outside common definitions and understandings of genre.

From Lost to Found

Design sharing sites provide UI designers with a platform to share their works and also an opportunity to get inspiration from others' designs. To facilitate management and search of millions of UI design images, many design sharing sites adopt collaborative tagging systems by distributing the work of categorization to the community. However, designers often do not know how to properly tag one design image with compact textual description, resulting in unclear, incomplete, and inconsistent tags for uploaded examples which impede retrieval, according to our empirical study and interview with four professional designers. Based on a deep neural network, we introduce a novel approach for encoding both the visual and textual information to recover the missing tags for existing UI examples so that they can be more easily found by text queries. We achieve 82.72% accuracy in the tag prediction. Through a simulation test of 5 queries, our system on average returns hundreds more results than the default Dribbble search, leading to better relatedness, diversity and satisfaction.

Expert Detection and Recommendation Model With User-Generated Tags in Collaborative Tagging Systems

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

Taylor's Law in Innovation Processes.

Taylor’s law quantifies the scaling properties of the fluctuations of the number of innovations occurring in open systems. Urn-based modeling schemes have already proven to be effective in modeling this complex behaviour. Here, we present analytical estimations of Taylor’s law exponents in such models, by leveraging on their representation in terms of triangular urn models. We also highlight the correspondence of these models with Poisson–Dirichlet processes and demonstrate how a non-trivial Taylor’s law exponent is a kind of universal feature in systems related to human activities. We base this result on the analysis of four collections of data generated by human activity: (i) written language (from a Gutenberg corpus); (ii) an online music website (Last.fm); (iii) Twitter hashtags; (iv) an online collaborative tagging system (Del.icio.us). While Taylor’s law observed in the last two datasets agrees with the plain model predictions, we need to introduce a generalization to fully characterize the behaviour of the first two datasets, where temporal correlations are possibly more relevant. We suggest that Taylor’s law is a fundamental complement to Zipf’s and Heaps’ laws in unveiling the complex dynamical processes underlying the evolution of systems featuring innovation.

Personalized resource recommendation method for collaborative tagging system based on machine learning

Personalized resource recommendation method for collaborative tagging system based on machine learning

Usage, captures, mentions, social media and citations of LIS highly cited papers: an altmetrics study

PurposeThe purpose of this paper is to investigate the usage, captures, mentions, social media and citations of highly cited papers of Library and information science (LIS).Design/methodology/approachThis study is quantitative research that was conducted using scientometrics and altmetrics indicators. The research sample consists of LIS classic papers. The papers contain highly cited papers of LIS that are introduced by Google Scholar. The research data have been gathered from Google Scholar, Scopus and Plum Analytics Categories. The data analysis has been done by Excel and SPSS applications.FindingsThe data indicate that among the highly cited articles of LIS, the highest score regarding the usage, captures, mentions and social media and the most abundance of citations belong to “Citation advantage of open access articles” and “Usage patterns of collaborative tagging systems.” Based on the results of Spearman statistical tests, there is a positive significant correlation between Google Scholar Citations and all studied indicators. However, only the correlation between Google Scholar Citations with capture metrics (p-value = 0.047) and citation metrics (p-value = 0.0001) was statistically significant.Originality/valueAltmetrics indicators can be used as complement traditional indicators of Scientometrics to study the impact of papers. Therefore, the Altmetrics knowledge of LIS researchers and experts and practicing new studies in this field will be very important.

Tag Clouds: Past, Present and Future

With the increase in popularity of collaborative tagging systems like Del.icio.us and Flickr, the need for an effective method to visualize information in these systems has become paramount. A tag cloud being currently the most popular and user-friendly interface provides insight into the information space of a collaborative tagging system but has several inherent limitations, resulting in some serious challenges in effective retrieval of relevant content. This paper initially presents a survey of the ongoing research efforts in evolving tag clouds from first generation of static nature to dynamic and more interactive second-generation interfaces. It further elaborates on the analysis of tag cloud design issues. It concludes with a set of recommendations for the design of next-generation visual interfaces to collaborative tagging applications. This work will prove useful to researchers who aim to design interfaces for information retrieval in folksonomy-based applications which satisfy users’ need for least cognitive load.

Will Too Many Editors Spoil The Tag?

Q&A websites compile useful knowledge through user-generated questions and responses. Many Q&As use collaborative tagging systems to improve search and discovery while distributing the work of categorizing and organization throughout the community. Although early work on collaborative tagging questioned whether consistent categorization schemes could emerge from large groups with little to no coordination, empirical studies have found surprising coherence among users' tags. We build on this research by testing whether coherence emerges in tag usage on Q&As, a more challenging context, focusing in particular on mismatches in the specificity of tags (basic level disagreement). We found that some users shifted toward more specific tag usage over time slightly increasing conflict, but that moderators were instrumental in helping to resolve some of this conflict. This study highlights the importance of learning and moderation in the development of coherence in collaborative tagging systems.

Folksonomy-based user profile enrichment using clustering and community recommended tags in multiple levels

Abstract Folksonomy (aka Collaborative tagging) systems provide a platform to the users where they can annotate a web resource by using any tag of interest. It is a first-hand information directly given by user without any middleman modification, therefore, it is more reliable than any other means. This paper proposes a novel methodology to construct a strong User Interest Profile (UIP) by exploiting user’s own activities and other activities occurring in user’s social network. UIP will provide a complete list of user preferences along with his level of interest in that preference. The proposed methodology is different from other strategies used for UIP enrichment as user’s own tags are not enough to construct a strong UIP. In the current research work, two strategies have been employed for the enrichment of UIP. First one is clustering of tags based on the concept of semantic relatedness between two tags in the real world. This has been measured using Word2vec model. The second one is the utilization of user’s real friendship network. It is believed that the present work is the first one to integrate the concept of semantic relatedness for tag clustering. The performance of proposed methodology has been evaluated on the basis of evaluation metrics i.e. MRR, imp, completeness and P@k using a dataset of del.icio.us. To analyse the impact of parameters, similarity measure and number of clusters in cluster set, on the performance of UIP constructed by proposed methodology extensive experiments are performed. The results reveal that the proposed methodology outperforms all the state of the art methodologies in terms of accurate and efficient UIP construction for every value of the parameters under consideration.

Personalized Search by a Multi-type and Multi-level User Profile in Folksonomy

With the development of the web 2.0 communities, more and more collaborative tagging systems become popular in recent years. Based on previous relevant works on the collaborative tagging system, this paper proposes a concept of a multi-type and multi-level user profile for improving the efficiency of personalized search. User profile consists of different types of resource attributes, and every type reflects multi-level favorites and nuisances from user. A detailed design process of user profile is presented in this paper. We propose a personalized search method by using the multi-type and multi-level user profile. Experimental results on a large real dataset demonstrate that the multi-type and multi-level user profile outperforms the baseline methods.

Social tagging strategy for enhancing e-learning experience

Success of e-learning systems depends on their capability to automatically retrieve and recommend relevant learning content according to the preferences of a specific learner. Learning experience and dynamic choice of educational material that is presented to learners can be enhanced using different recommendation techniques. As popularity of collaborative tagging systems grows, users’ tags could provide useful information to improve recommender system algorithms in e-learning environments. In this paper, we present an approach for implementation of collaborative tagging techniques into online tutoring system. The implemented approach combines social tagging and sequential patterns mining for generating recommendations of learning resources to learners. Several experiments were carried out in order to verify usability of the proposed hybrid method within e-learning environment and analyze selected social tagging techniques.

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

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

A Tag-based Recommendation Algorithm Integrating Short-term and Long-term Interests of Users

In the collaborative tagging system, tags of users contain rich information on personalized preference, and time stamps of users show their interest changes. Users’ interest has the varied timeliness, but the existing recommendation algorithm fusing time information only emphasizes the short-term interest of users but fails to dig into the long-term stable interest of users and thus presents the low recommendation precision. In this paper, we propose a tag-based recommendation algorithm integrating short-term and long-term interests of users. The algorithm first builds the short-term and long-term interest characteristics of users based on tags according to the use frequency, time decay, life cycle and volatility; then it maps the characteristics to the resources tagged by the user to form a user-resource pseudo-scoring matrix. Finally, it calculates the set of the nearest neighbors of user with the matrix to give recommendations. The verification on the delicious data set shows that the algorithm improves recommendation precision and diversity compared with other classical algorithms.

LinearTag Models: Recommendations Using Linear User Profiles Based on Tags

Recommender systems allow the exploration of large collections of products, the discovery of patterns in the products, and the guidance of users towards products that match their interests. Collaborative tagging systems allow users to label products in a collection using a free vocabulary. The aggregation of these tags, also called a Folksonomy, can be used to build a collective characterization of the products in a simple and recognizable vocabulary. In this paper, we propose a family of methods called LinearTag recommenders, which infer users preferences for tags to formulate recommendations for them. We dubbed these inferred user profiles as TagProfiles. We present experiments using them as an interaction artifact that allows users to receive new recommendations as they delete, add or reorder tags in their profiles. Additional experiments using the Movielens dataset, show that the proposed methods generate recommendations with an error margin similar, or even lower than the results reported by methods based on latent factors. Next, we compared TagProfiles against KeywordProfiles, which are profiles based on keywords extracted automatically from textual descriptions of products. This comparison showed that TagProfiles are not only more precise in their predictions, but they are also more understandable by users. At last, we developed a user interface of a movie recommender based on TagProfiles, which we tested with 25 users. This experience showed that TagProfiles are easier to understand and modify by users, allowing them to discover new movies as they interact with their profiles.

Folksonomy-based personalized search by hybrid user profiles in multiple levels

Recently, some systems have allowed users to rate and annotate resources, e.g., MovieLens, and we consider that it provides a way to identify favorite and non-favorite tags of a user by integrating his or her rating and tags. In this paper, we review and elaborate on the limitations of the current research on user profiling for personalized search in collaborative tagging systems. We then propose a new multi-level user profiling model by integrating tags and ratings to achieve personalized search, which can reflect not only a user׳s likes but also a his or her dislikes. To the best of our knowledge, this is the first effort to integrate ratings and tags to model multi-level user profiles for personalized search.

Analysis of the Impact of a Tag Recommendation System in a Real-World Folksonomy

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.

Folksonomy-Based Recommender Systems: A State-of-the-Art Review

Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open-ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy-based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link-analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.