Characterizing Queries in Different Search Tasks

Kim, Jeonghyun; Can, Ahmet Vecdi

doi:10.1109/hicss.2012.150

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…According to the different QoS requirement in the process of user queries, there are commonly two steps [20]. Because the users have not any priori knowledge of the system and they firstly want to get some understanding about the system, or the users could not insure that the search criteria was suitable for themselves.…”

Section: The Workflowmentioning

confidence: 99%

Using provenance to efficiently improve metadata searching performance in storage systems

Liu

Feng

Hua

et al. 2015

Future Generation Computer Systems

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Section: The Workflowmentioning

confidence: 99%

Using provenance to efficiently improve metadata searching performance in storage systems

Liu

Feng

Hua

et al. 2015

Future Generation Computer Systems

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“…Our work falls into the former category, in contrast to important recent work, which falls into the latter category. Important recent work from the field of information retrieval are Mastora et al [21], who showed that failed queries represent 36% of overall submitted queries, and Kim and Can [16], who investigated failure as a factor characterizing queries in different search tasks. In the field of multimedia retrieval, Pu [22] compared characteristics of successful and failed image queries and Kofler et al [17] investigated video search failure search using expressions of failed user needs posted on a social forum.…”

Section: Related Work 21 Search Failurementioning

confidence: 99%

When video search goes wrong

Kofler

Yang

Larson

et al. 2012

Proceedings of the 20th ACM International Conference on Multimedia

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The recent increase in the volume and variety of video content available online presents growing challenges for video search. Users face increased difficulty in formulating effective queries and search engines must deploy highly effective algorithms to provide relevant results. Although lately much effort has been invested in optimizing video search engine results, relatively little attention has been given to predicting for which queries results optimization is most useful, i.e., predicting which queries will fail. Being able to predict when a video search query would fail is likely to make the video search result optimization more efficient and effective, improve the search experience for the user by providing support in the query formulation process and in this way boost the development of video search engines in general. While insight about a query's performance in general could be obtained using the well-known concept of query performance prediction (QPP), we propose a novel approach for predicting a failure of a video search query in the specific context of a search session. Our context-aware query failure prediction approach uses a combination of user indicators and engine indicators to predict whether a particular query is likely to fail in the context of a particular search session. User indicators are derived from the search log and capture the patterns of query (re)formulation behavior and the click-through data of a user during a typical video search session. Engine indicators are derived from the video search results list and capture the visual variance of search results that would be offered to the user for the given query. We validate our approach experimentally on a test set containing 1+ million video search queries and show its effectiveness compared to a set of conventional QPP baselines. Our approach achieves a 13% relative improvement over the baseline. Figure 1: A visualization of an excerpt from an ongoing search session from the transaction log. The query 'new york' was not successful, gets reformulated to 'new york alicia keys' leading to a query success.

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