Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the noise that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.
Cited By
Chen Y, Dong H and Wang W Topic-graph based recommendation on social tagging systems Proceedings of the 2018 International Conference on Data Science and Information Technology, (138-143)- Kopeinik S, Lex E, Seitlinger P, Albert D and Ley T Supporting collaborative learning with tag recommendations Proceedings of the Seventh International Learning Analytics & Knowledge Conference, (409-418)
- Tang X, Xu Y and Geva S Learning Higher-Order Interactions for User and Item Profiling Based on Tensor Factorization Proceedings of the 20th International Conference on Intelligent User Interfaces, (213-224)
- Tang X, Xu Y and Geva S Refining User and Item Profiles based on Multidimensional Data for Top-N Item Recommendation Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services, (310-319)
- Tang X, Xu Y and Geva S Tensor Reduction for User Profiling in Personalized Recommender Systems Proceedings of the 19th Australasian Document Computing Symposium, (34-41)
- Zhang Z, Zeng D, Abbasi A, Peng J and Zheng X (2013). A Random Walk Model for Item Recommendation in Social Tagging Systems, ACM Transactions on Management Information Systems (TMIS), 4:2, (1-24), Online publication date: 1-Aug-2013.
- Biancalana C, Gasparetti F, Micarelli A and Sansonetti G (2013). Social semantic query expansion, ACM Transactions on Intelligent Systems and Technology (TIST), 4:4, (1-43), Online publication date: 1-Sep-2013.
- van Leeuwen M and Puspitaningrum D Improving tag recommendation using few associations Proceedings of the 11th international conference on Advances in Intelligent Data Analysis, (184-194)
Index Terms
- Recommender Systems for Social Tagging Systems
Recommendations
Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions
Social 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 ...
Exploiting Social Tagging in a Web 2.0 Recommender System
Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take ...