Computing the tag genome (original) (raw)
Related papers
The Tag Genome: Encoding Community Knowledge to Support Novel Interaction
Acm Transactions on Interactive Intelligent Systems, 2012
This article introduces the tag genome, a data structure that extends the traditional tagging model to provide enhanced forms of user interaction. Just as a biological genome encodes an organism based on a sequence of genes, the tag genome encodes an item in an information space based on its relationship to a common set of tags. We present a machine learning approach for computing the tag genome, and we evaluate several learning models on a ground truth dataset provided by users. We describe an application of the tag genome called Movie Tuner which enables users to navigate from one item to nearby items along dimensions represented by tags. We present the results of a 7-week field trial of 2,531 users of Movie Tuner and a survey evaluating users' subjective experience. Finally, we outline the broader space of applications of the tag genome.
2009
Abstract Web 2.0 users engage in collaborative tagging and associate words with the users' own and with other users' content. The result is an additional layer with descriptions of web resources. The information contained in the tag-resource associations can certainly be useful. However, we argue that current collaborative tagging systems do not support users well in navigating and exploring tag spaces, because tags are treated as independent.
Creating tag hierarchies for effective navigation in social media
… of the 2008 ACM workshop on …, 2008
In social media, such as blogs, since the content naturally evolves over time, it is hard or in many cases impossible to organize the content for effective navigation. Thus, one commonly has to resort to simple tools, such as tags and tag clouds, for presenting frequently used keywords to users to provide them at least some high level idea about the content of a given set of social media entries. Most visualizations of tag clouds vary the sizes of the fonts to differentiate important tags from those that are less important. We propose an alternative "contextual-layout" method, TMine, for analyzing and presenting tags that are extracted from textual content. In TMine tags are first mapped onto a latent semantic space. Then, TMine analyzes the relationships between tags relying on an extended boolean interpretation of the semantic space. The tag cloud is condensed into a hierarchy in a way that captures contextual relationships between tags: in particular, descendant terms in the hierarchy occur within the context defined by the ancestor terms. This provides a mechanism for navigation within the tag space as well as for classification of the text documents based on the contextual structure implied by the tags.
Tag Trails: Navigating with Context and History
We describe a technique for preserving and presenting context and history while navigating web resources described by keywords. We use tagging and tag clouds as an application area for our technique. The technique is illustrated by employing it in a prototype that interfaces data from a social tagging website used to bookmark academic articles. The prototype displays a “tag trail” which can reveal contextual connections between web resources and the associated tags. We argue that the user’s understanding of web resources is aided by making such connections explicit. PDF: http://cli.gs/ngDRTM
An IR-Based Approach for Tag Recommendation
2010
Thanks to the continuous growth of collaborative platforms like YouTube, Flickr and Delicious, we are recently witnessing to a rapid evolution of web dynamics towards a more 'social' vision, called Web 2.0. In this context collaborative tagging systems are rapidly emerging as one of the most promising tools. However, as tags are handled in a simply syntactical way, collaborative tagging systems suffer of typical Information Retrieval (IR) problems like polysemy and synonymy: so, in order to reduce the impact of these drawbacks and to aid at the same time the so-called tag convergence, systems that assist the user in the task of tagging are required.
LinearTag Models: Recommendations Using Linear User Profiles Based on Tags
Computación y Sistemas
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.
Proceedings of the 18th international conference on World wide web - WWW '09, 2009
Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collections of items. Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the flexibility and conceptual comprehensibility inherent in tagging systems. In this paper we explore tagommenders, recommender algorithms that predict users' preferences for items based on their inferred preferences for tags. We describe tag preference inference algorithms based on users' interactions with tags and movies, and evaluate these algorithms based on tag preference ratings collected from 995 MovieLens users. We design and evaluate algorithms that predict users' ratings for movies based on their inferred tag preferences. Our tag-based algorithms generate better recommendation rankings than state-of-the-art algorithms, and they may lead to flexible recommender systems that leverage the characteristics of items users find most important.
Enhancing the navigability of social tagging systems with tag taxonomies
Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies - i-KNOW '11, 2011
Tagging introduces an additional intuitive and easy method to organize resources in information systems. Although tags exhibit useful properties for e.g. personal organization of information, recent research has shown that the navigability of social tagging systems leaves much to be desired. When browsing social tagging systems users often have to navigate through huge lists of potential results before arriving at the desired resource. Thus, from a user point of view tagging systems are typically hard to navigate.