Sheffield Submission to the CHiC Ineractive Task: Exploring Digital Cultural Heritage

The Cultural Heritage in CLEF 2013 (CHiC) interactive task focused on acquiring and analysing interactive information retrieval (IIR)behaviour in a Digital Cultural Heritage collection. The University of Sheffield contributed 120 on-line and 20 in-lab participants to this task. The results of both the on-line and in-lab experiments strongly indicate that when faced with a new, unfamiliar collection and an open-ended task, participants will spend more time using the category hierarchy for exploration, than the search box. However, analysis of the the number of items the on-line participants view in detail and then saved to their workspace indicates that the two access methods fulfil different functions. From this we conclude that the categories are seemingly there to support the development of an initial overview over the collection, while the search is used to locate things in a more focused manner

Overview of the INEX 2014 Interactive Social Book Search Track

Abstract. Users looking for books online are confronted with both pro-fessional meta-data and user-generated content. The goal of the Interac-tive Social Book Search Track was to investigate how users used these two sources of information, when looking for books in a leisure context. To this end participants recruited by four teams performed two different tasks using one of two book-search interfaces. Additionally one of the two interfaces also investigated whether user performance can be improved by providing a user-interface that supports multiple search stages.

Enhancing Translation Language Models with Word Embedding for Information Retrieval

In this paper, we explore the usage of Word Embedding semantic resources for Information Retrieval (IR) task. This embedding, produced by a shallow neural network, have been shown to catch semantic similarities between words (Mikolov et al., 2013). Hence, our goal is to enhance IR Language Models by addressing the term mismatch problem. To do so, we applied the model presented in the paper Integrating and Evaluating Neural Word Embedding in Information Retrieval by Zuccon et al. (2015) that proposes to estimate the translation probability of a Translation Language Model using the cosine similarity between Word Embedding. The results we obtained so far did not show a statistically significant improvement compared to classical Language Model

Effective metadata for social book search from a user perspective

Abstract. In this extended abstract we describe our participation in the INEX 2014 Interactive Social Book Search Track. In previous work, we have looked at the impact of professional and user-generated metadata in the context of book search, and compared these different categories of metadata in terms of retrieval effectiveness. Here, we take a different approach and study the use of professional and user-generated metadata of books in an interactive setting, and the effectivity of this metadata from a user perspective. We compare the perceived usefulness of general descriptions, publication metadata, user reviews and tags in focused and open-ended search tasks, based on data gathered in the INEX Interactive Social Book Search Track. Furthermore, we take a tentative look at the actual use of different types of metadata over time in the aggregated search tasks. Our preliminary findings in the surveyed tasks indicate that user reviews are generally perceived to be more useful than other types of metadata, and they are frequently mentioned in users ’ rationales for selecting books. Furthermore, we observe a varying usage frequency of traditional and user-generated metadata across time in the aggregated search tasks, pro-viding initial indications that these types of metadata might be useful at different stages of a search task.