**Abstract**

Tagging has emerged as one of the best ways of associating metadata with objects (e.g., videos, texts) in Web 2.0 applications. Consisting of freely chosen keywords assigned to objects by users, tags represent a simpler, cheaper, and a more natural way of organizing content than a fixed taxonomy with a controlled vocabulary. Moreover, recent studies have demonstrated that among other textual features such as title, description, and user comments, tags are the most effective to support information retrieval (IR) services such as search, automatic classification, and content recommendation. In this context, tag recommendation services aim at assisting users in the tagging process, allowing users to select some of the recommended tags or to come up with new ones. Besides improving user experience, tag recommendation services potentially improve the quality of the generated tags, benefiting IR services that rely on tags as data sources. Besides the obvious benefit of improving the description of the objects, tag recommendation can be directly applied in IR services such as search and query expansion. In this chapter, we will provide the main concepts related to tagging systems, as well as an overview of tag recommendation techniques, dividing them into two stages of the tag recommendation process: (1) the candidate tag extraction and (2) the candidate tag ranking.

**Keywords:** tagging, folksonomies, Web 2.0, tag recommendation, keyword extraction, tag ranking

#### **1. Introduction**

Web 2.0 applications are characterized by the central role played by users in the creation and sharing of their own content. Tagging has become a common feature available in these applications, consisting in associating freely created tags (keywords) to objects (e.g., videos, images, texts). In comparison with a fixed taxonomy, tags are simpler, cheaper, and a more natural way of organizing content. In fact, taxonomies with a controlled vocabulary do not suit the increasing and evolving Web 2.0 environment [1].

Moreover, various studies have demonstrated that, among other textual features such as title, description, and user comments, tags are the most effective to support information retrieval (IR) services such as search [2], automatic classification [3], and content recommendation [4].

The tagging process can benefit a lot from a tag recommendation service. This type of service supports users in the selection of some of the recommended tags or in the creation of new ones. With that in mind, tag recommendation benefits are not limited

to the improvement of the user experience: there is a high potential of improving the quality of the generated tags by, for example, reducing the amount of misspellings and nondescriptive keywords. Thus, the quality of the IR services that rely on tags as data sources can be indirectly improved by tag recommendation. Other examples of the benefits that tag recommendation can bring to IR services include the direct application of the recommended tags in search [5] and on query expansion [6]. In search, the recommended tags can be exploited to measure the similarity between queries and documents, improving the quality of the retrieved documents. Query expansion, in turn, aims at suggesting more specific and unambiguous queries to the user, which also allows the achievement of better search results. Further examples include researcher profile summarization [7] and search result summarization [8].

Tag recommendation brings specific challenges that other kinds of recommendation services do not: in the tag domain, we are interested not only in matching the interests of the target user but also in describing, summarizing, and organizing Web content. Thus, the design of tag recommenders demands specific solutions which greatly differ from methods proposed for item recommendation tasks in general. For instance, text mining, knowledge extraction, and semantics play a substantial role in the tag domain. In sum, the recommendation effectiveness affects not only user satisfaction but also the performance of various IR services that rely on tags as data source.

The goal of this chapter is to present the concepts of tagging systems and to provide an overview of tag recommendation techniques, explaining the two main steps of these methods: the candidate tag generation and the candidate tag ranking.

The rest of this chapter is organized as follows. In Section 2, we define tags, objects, folksonomies, and other basic concepts related to tagging systems. In Section 3, we state the tag recommendation problem, while we explain the main tag candidate extraction and ranking techniques in Sections 4 and 5, respectively.
