## 2 Related Work
Most importantly, such systems should provide high quality recommendations, be adaptable to the individual needs of users, be lightweight enough to be highly usable and take advantage of the collective knowledge of the user community. These principles were taken as axioms for the development of the system described in this paper. Their investigation of related work reaffirms that tagging patterns in large communities stabilize over time, exhibiting clear structures that may be exploited for tag recommendation. Also, similarly to [PERSON] and [PERSON] [22], they consider the social aspects but also note that while tagging is a viable concept in the long term, the majority of web content is currently untagged. [PERSON] and [PERSON] [21] have developed an interactive tagging system based on the concept of incorporating user feedback using the general information retrieval techniques of term-based, tag-based and tag-collocation relevance feedback. The core of their system may be summarized as follows:  
1. Determine the top-_k_ most relevant objects with respect to the target object \(o\). 2. Extract and rank tags from these objects. 3. Allow the user to accept or reject the extracted tags. 4. Revise the description of \(o\) and repeat steps 1-4 until a stopping condition is met. 5. Optionally, allow the user to add additional tags that were not suggested by the system. [PERSON] and [PERSON] went on to implement the process in a service-based interactive tagging framework known as _Plurality_. The system allows documents to be tagged interactively using a model based on the well-known _vector space_ model [23]. When a document is first submitted, the system recommends tags using the nearest neighbor paradigm operating on tuples of the form _(User, Document, Tag)_, comparable to the structure presented in [24]. The documents themselves are compared using _cosine similarly_ and _term frequency_--_inverse document frequency_ (TF-IDF), which have also been borrowed from classical information retrieval. The tags are suggested by weighing them proportionally to how often they were used on similar documents. This methodology also forms the basis for the _N-Closest Photos (NCP)_ model described in Section 4. The second element of the Plurality system is the _associated feedback_ model. During development, three different models were examined for giving feedback on the suggested tags:  
* _Tag feedback_. After receiving the proposals, users are given the opportunity to classify each tag as _relevant_ or _irrelevant_ to the document. The system then uses the information to re-retrieve better matching documents to compute (hopefully) better suggestions. * _Term feedback_. In this model, users rate the terms extracted from the document rather than the resulting tags, once again with the aim of retrieving more relevant documents. * _Tag collocation (co-occurrence)_. The system exploits the collective intelligence of the user community by finding tags that were used together with tags the user rated as relevant. Despite the fact that user feedback is not explicitly considered here, the proposed use scenario indirectly exploits both tag feedback and co-occurrence. In our system (as described in more detail in Section 4), users can accept or reject tags (_i.e._ provide tag feedback) after each iteration. These are then used as start tags in following cycles to further refine the process. Tag co-occurrence, on the other hand, is exploited to provide the tagging vocabulary: only tags that co-occur with start tags may be suggested. [PERSON] and [PERSON] evaluated their system by recording the tagging sessions of 200 participants who were required to use the system to tag documents on _Delicious_ ([[http://del.icio.us](http://del.icio.us)]([http://del.icio.us](http://del.icio.us))). Although they found that users working with the tag feedback model consistently required the least steps to tag documents, the tag co-occurrence model maximized the ratio of selected tags to contributed tags.