Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for a given item. In this paper we propose an adaptation of the search algorithms proposed in [14, 1] to the tag recommendation problem. Our algorithm, called STRec, provides networkaware recommendations based on proximity measures computed on-the-fly in the network. STRec uses a bounded s...

In this paper, we formulate image annotation as a Multi-correlation Learning to Rank (MLRank) problem, i.e., ranking the relevance of tags to an image considering the visual similarity and the semantic relevance. Unlike typical learning to rank algorithms, which assume that the ranking objects are independent, we attempt to rank relational data by exploring the consistency between “visual simil...

objective(s) human papillomavirus (hpv) infections are related to the genesis of various benign lesions and some malignant tumors, but no clear relationship has been identified so far between the subtypes of hpv and skin tag. materials and methods the present case-control study was designed to detect the existence of low risk and high risk hpv types in lesions of 50 patients with skin tag (case...

Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for tagging an item. In this paper we present a part of our work in progress which is a novel improvement of recommendations by re-ranking the output of a tag recommender. We mine association rules between candidates tags in order to determine a more consistent list of tags to recommend. O...

We investigate the generation of tag clouds using Bayesian models and test the hypothesis that social network information is better than overall popularity for ranking new and relevant information. We propose three tag cloud generation models based on popularity, topics and social structure. We conducted two user evaluations to compare the models for search and recommendation of music with soci...

Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for tagging an item. In this paper we present a part of our work in progress which is a novel improvement of recommendations by re-ranking the output of a tag recommender. We mine association rules between candidates tags in order to determine a more consistent list of tags to recommend. O...

Tagging is nowadays the most prevalent and practical way to make images searchable. However, in reality many manually-assigned tags are irrelevant to image content and hence are not reliable for applications. A lot of recent efforts have been conducted to refine image tags. In this paper, we propose to do tag refinement from the angle of topic modeling and present a novel graphical model, regul...

Anchor text has been shown to be effective in ranking[6] and a variety of information retrieval tasks on web pages. Some authors have expanded on anchor text by using the words around the anchor tag, a link-context, but each with a different definition of link-context. This lack of consensus begs the question: What is a good link-context? The two experiments in this paper address the question b...

In this paper we report on MICC participation to the Scalable Concept Image Annotation subtask of the ImageCLEF Photo Annotation and Retrieval competition [13]. Our goal has been to investigate the applicability of data-driven methods that have obtained good results in the field of social image annotation and retrieval to web images. These methods have been applied typically to tasks such as ta...

Many image sharing websites, e.g. Flickr, Google+, allow users to upload images as an event, and users can browse the images others uploaded as events. The fact that people usually browse only the first few images of an event then decide whether the event is what they want makes us believe that it is necessary to present those images people favor on the very first position for each event. Here ...