Search (5 results, page 1 of 1)

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Abstract

The Initiative for the Evaluation of XML retrieval (INEX) provides a TREC-like platform for evaluating content-oriented XML retrieval systems. Since 2007, INEX has been using a set of precision-recall based metrics for its ad hoc tasks. The authors investigate the reliability and robustness of these focused retrieval measures, and of the INEX pooling method. They explore four specific questions: How reliable are the metrics when assessments are incomplete, or when query sets are small? What is the minimum pool/query-set size that can be used to reliably evaluate systems? Can the INEX collections be used to fairly evaluate "new" systems that did not participate in the pooling process? And, for a fixed amount of assessment effort, would this effort be better spent in thoroughly judging a few queries, or in judging many queries relatively superficially? The authors' findings validate properties of precision-recall-based metrics observed in document retrieval settings. Early precision measures are found to be more error-prone and less stable under incomplete judgments and small topic-set sizes. They also find that system rankings remain largely unaffected even when assessment effort is substantially (but systematically) reduced, and confirm that the INEX collections remain usable when evaluating nonparticipating systems. Finally, they observe that for a fixed amount of effort, judging shallow pools for many queries is better than judging deep pools for a smaller set of queries. However, when judging only a random sample of a pool, it is better to completely judge fewer topics than to partially judge many topics. This result confirms the effectiveness of pooling methods.

Date

22. 1.2011 14:20:56

Type

a

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Type

a

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Type

a

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Abstract

This article studies one of the main bottlenecks in providing more effective information access: the poverty on the query end. We explore whether users can classify keyword queries into categories from the DMOZ directory on different levels and whether this topical context can help retrieval performance. We have conducted a user study to let participants classify queries into DMOZ categories, either by freely searching the directory or by selection from a list of suggestions. Results of the study show that DMOZ categories are suitable for topic categorization. Both free search and list selection can be used to elicit topical context. Free search leads to more specific categories than the list selections. Participants in our study show moderate agreement on the categories they select, but broad agreement on the higher levels of chosen categories. The free search categories significantly improve retrieval effectiveness. The more general list selection categories and the top-level categories do not lead to significant improvements. Combining topical context with blind relevance feedback leads to better results than applying either of them separately. We conclude that DMOZ is a suitable resource for interacting with users on topical categories applicable to their query, and can lead to better search results.

Type

a

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Abstract

Due to the advances in information retrieval in the past decades, search engines have become extremely efficient at acquiring useful sources in response to a user's query. However, for more prolonged and complex information seeking tasks, these search engines are not as well suited. During complex information seeking tasks, various stages may occur, which imply varying support needs for users. However, the implications of theoretical information seeking models for concrete search user interfaces (SUI) design are unclear, both at the level of the individual features and of the whole interface. Guidelines and design patterns for concrete SUIs, on the other hand, provide recommendations for feature design, but these are separated from their role in the information seeking process. This chapter addresses the question of how to design SUIs with enhanced support for the macro-level process, first by reviewing previous research. Subsequently, we outline a framework for complex task support, which explicitly connects the temporal development of complex tasks with different levels of support by SUI features. This is followed by a discussion of concrete system examples which include elements of the three dimensions of our framework in an exploratory search and sensemaking context. Moreover, we discuss the connection of navigation with the search-oriented framework. In our final discussion and conclusion, we provide recommendations for designing more holistic SUIs which potentially evolve along with a user's information seeking process.