Improving tag recommendation using social networks

In this paper we address the task of recommending additional tags to partially annotated media objects, in our case images. We propose an extendable framework that can recommend tags using a combination of different personalised and collective contexts. We combine information from four contexts: (1) all the photos in the system, (2) a user's own photos, (3) the photos of a user's social contacts, and (4) the photos posted in the groups of which a user is a member. Variants of methods (1) and (2) have been proposed in previous work, but the use of (3) and (4) is novel. For each of the contexts we use the same probabilistic model and Borda Count based aggregation approach to generate recommendations from different contexts into a unified ranking of recommended tags. We evaluate our system using a large set of real-world data from Flickr. We show that by using personalised contexts we can significantly improve tag recommendation compared to using collective knowledge alone. We also analyse our experimental results to explore the capabilities of our system with respect to a user's social behaviour

Queries: INEX 2003 working group report

This paper summarizes the discussion of the queries working group at INEX 2003. The group discussed both ContentOnly (CO) and Content-And-Structure (CAS) queries. Discussion was however mainly on CAS query syntax, CAS target elements and future CAS data types. The queries working group consisted of: Holger Florke, Norbert Fuhr, Kenji Hatano, Borkur Sigurbjornsson, Andrew Trotman, Masahiro Watanabe Content Only Topics There was little discussion on CO topics in the working group. This is to be interpreted as a support for leaving the CO topic format unchanged for at least next yea

What do users think of an XML element retrieval system

the INEX 2005 Interactive Track, mainly focusing on a comparative experiment, in which the baseline system Daffodil/HyREX is compared to a home-grown XML element retrieval system (xmlfind). The xmlfind system provides an interface for an XML information retrieval search engine, using an index that contains all the individual XML elements in the IEEE collection. Our main findings are the following. First, test persons show appreciation for both systems, but xmlfind receives higher scores than Daffodil. Second, the interface seems to take the structural dependencies between retrieved elements into account in an appropriate way: although retrieved elements may be overlapping in whole or in part, none of the test persons regarded this as problematic. Third, the general opinion of the test persons on the usefulness of XML retrieval systems was unequivocally positive, and their responses highlight many of the hoped advantages of an XML retrieval system.

The effect of structured queries and selective indexing on XML retrieval

the INEX 2005 ad hoc track, covering the Thorough, Focused, and Fetch-Browse tasks and their structured (+S) counterparts. Our research questions for this round of INEX were threefold. Our first and main research question was to investigate the contribution of structural constraints to improved retrieval performance. Our main results were that the two types of structural constraints have different effects. Constraining the target of result elements gives improvements in terms of early precision. Constraining the context of result elements improves mean average precision. Our second research question was to experiment with selective indexing strategies based on either the length of elements, the tag-name of elements considered relevant in earlier INEX years, or simply by indexing all sections or articles. Our experiments show that disregarding 80–90% of the total number of elements does not decrease retrieval performance. Third, we considered the automatic creation of structured queries using blind feedback. Here, our results are inconclusive, mainly due to few queries used and lack of comparison to traditional blind feedback.

ABSTRACT Processing Content-Oriented XPath Queries

Document-centric XML collections contain text-rich documents, marked up with XML tags that add lightweight semantics to the text. Querying such collections calls for a hybrid query language: the text-rich nature of the documents suggests a content-oriented (IR) approach, while the mark-up allows users to add structural constraints to their IR queries. Hybrid queries tend to be more expressive, which should lead—in principle—to better retrieval performance. In practice, the processing of these hybrid queries within an IR systems turns out to be far from trivial, because a delicate balance between structural and content information needs to be sought. We propose an approach to processing such hybrid content-and-structure queries that decomposes a query into multiple content-only queries whose results are then combined in ways determined by the structural constraints of the original query. We evaluate our methods using the INEX 2003 test-suite, and show (1) that effective ways of processing of content-oriented XPath queries are non-trivial, (2) that there are differences in the effectiveness for different topics types, but (3) that with appropriate processing methods retrieval effectiveness can improve

Flickr tag recommendation based on collective knowledge

Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide additional contextual and semantical information. In this paper we investigate how we can assist users in the tagging phase. The contribution of our research is twofold. We analyse a representative snapshot of Flickr and present the results by means of a tag characterisation focussing on how users tags photos and what information is contained in the tagging. Based on this analysis, we present and evaluate tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo. The results of the empirical evaluation show that we can effectively recommend relevant tags for a variety of photos with different levels of exhaustiveness of original tagging