Automatic categorization of query results

Chakrabarti, Kaushik; Chaudhuri, Surajit; Hwang, Seung-won

doi:10.1145/1007568.1007653

Cited by 65 publications

(55 citation statements)

References 13 publications

(15 reference statements)

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“…For a given query, these approaches compute the best facet conditions and matching results to display to the user [12][21] [22]. However, this model is not suitable for our setting of limited screen size, where we cannot display separately faceted conditions and results.…”

Section: Related Workmentioning

confidence: 99%

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User effort minimization through adaptive diversification

Hasan

Kashyap

Hristidis

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Ambiguous queries, which are typical on search engines and recommendation systems, often return a large number of results from multiple interpretations. Given that many users often perform their searches on limited size screens (e.g. mobile phones), an important problem is which results to display first. Recent work has suggested displaying a set of results (Top-k) based on their relevance score with respect to the query and their diversity with respect to each other. However, previous works balance relevance and diversity mostly by a predefined fixed way. In this paper, we show that for different search tasks there is a different ideal balance of relevance and diversity. We propose a principled method for adaptive diversification of query results that minimizes the user effort to find the desired results, by dynamically balancing the relevance and diversity at each query step (e.g. when refining the query or viewing the next page of results). We introduce a navigation cost model as a means to estimate the effort required to navigate the queryresults, and show that the problem of estimating the ideal amount of diversification at each step is NP-Hard. We propose an efficient approximate algorithm to select a near-optimal subset of the query results that minimizes the expected user effort. Finally we demonstrate the efficacy and efficiency of our solution in minimizing user effort, compared to state-of-the-art ranking methods, by means of an extensive experimental evaluation and a comprehensive user study on Amazon Mechanical Turk.

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Section: Related Workmentioning

confidence: 99%

“…Such navigation models, where the user only selects actions proposed by the system, have been used extensively in the navigation of keyword-based query results [12][21] [22]. Each user action (REFINE, READ-RESULT, NEXT-PAGE) is an effort on the part of the user.…”

mentioning

confidence: 99%

User effort minimization through adaptive diversification

Hasan

Kashyap

Hristidis

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

show abstract

“…Especially biomedical data and the literature pertaining to it that reviews the aspects of biomedical data across the globe have seen tremendous growth in terms of quantity. Biological data sources such as [2], [3], and [4] are growing in terms of lakhs of new citations every year. The queries made by people associated with healthcare domain have to search such databases by providing a search keyword.…”

Section: Introductionmentioning

confidence: 99%

“…The navigation cost is more as user has to spend lot of time in finding the required subset of rows from the bulk of search results. This problem has been researched in [2], [3], [4] and the problem is identified as information overload. Figure 1 shows static navigation of MeSh hierarchy of biomedical data.…”

Section: Introductionmentioning

confidence: 99%