No gender differences in egocentric and allocentric environmental transformation after compensating for male advantage by manipulating familiarity

The present study has two-fold aims: to investigate whether gender differences persist even when more time is given to acquire spatial information; to assess the gender effect when the retrieval phase requires recalling the pathway from the same or a different reference perspective (egocentric or allocentric). Specifically, we analyse the performance of men and women while learning a path from a map or by observing an experimenter in a real environment. We then asked them to reproduce the learned path using the same reference system (map learning vs. map retrieval or real environment learning vs. real environment retrieval) or using a different reference system (map learning vs. real environment retrieval or vice versa). The results showed that gender differences were not present in the retrieval phase when women have the necessary time to acquire spatial information. Moreover, using the egocentric coordinates (both in the learning and retrieval phase) proved easier than the other conditions, whereas learning through allocentric coordinates and then retrieving the environmental information using egocentric coordinates proved to be the most difficult. Results showed that by manipulating familiarity, gender differences disappear, or are attenuated in all conditions

DCU search runs at MediaEval 2012: search and hyperlinking task

We describe the runs for our participation in the Search sub-task of the Search and Hyperlinking Task at MediaEval 2012. Our runs are designed to form a retrieval baseline by using time-based segmentation of audio transcripts incorporating pause information and a sliding window to define the retrieval segments boundaries with a standard language modelling information retrieval strategy. Using this baseline system runs based on transcripts provided by LIUM were better for all evaluation metrics, than those using transcripts provided by LIMSI

Personalization via collaboration in web retrieval systems: a context based approach

World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization

Mining user activity as a context source for search and retrieval

Nowadays in information retrieval it is generally accepted that if we can better understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval

A study of remembered context for information access from personal digital archives

Retrieval from personal archives (or Human Digital Memories (HDMs)) is set to become a significant challenge in information retrieval (IR) research. These archives are unique in that the items in them are personal to the owner and as such the owner may have personal memories associated with the items. It is recognized that the harnessing of an individual’s memories about HDM items can be used as context data (such as user location at the time of item access) to aid retrieval. We present a pilot study, using one subject’s HDM, of remembered context data and its utility in retrieval. Our results explore the types of context data best remembered for different item types and categories over time and show that context appears to become a more important factor in effective HDM IR over time as the subject’s recall of contents declines

Index ordering by query-independent measures

Conventional approaches to information retrieval search through all applicable entries in an inverted file for a particular collection in order to find those documents with the highest scores. For particularly large collections this may be extremely time consuming. A solution to this problem is to only search a limited amount of the collection at query-time, in order to speed up the retrieval process. In doing this we can also limit the loss in retrieval efficacy (in terms of accuracy of results). The way we achieve this is to firstly identify the most “important” documents within the collection, and sort documents within inverted file lists in order of this “importance”. In this way we limit the amount of information to be searched at query time by eliminating documents of lesser importance, which not only makes the search more efficient, but also limits loss in retrieval accuracy. Our experiments, carried out on the TREC Terabyte collection, report significant savings, in terms of number of postings examined, without significant loss of effectiveness when based on several measures of importance used in isolation, and in combination. Our results point to several ways in which the computation cost of searching large collections of documents can be significantly reduced