Aspect-based Query Expansion for Search Results Diversification

Shajalal, Md; Aono, Masaki; Azim, Muhammad Anwarul

doi:10.1109/iciev.2018.8641004

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…This method is learned through a general ranking model (gLTR) and a diversity-biased model. The word2vec model has been proven to be effective in measuring the similarity between documents and therefore used in state-ofthe-art SRD research (Shajalal et al, 2018;Ullah et al, 2016), which inspired the authors to compare the most popular algorithms in this paper.…”

Section: Research On Diversification For Re-rankingmentioning

confidence: 99%

Exploring multiple diversification strategies for academic citation contexts recommendation

Chen

Yang²,

et al. 2020

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Purpose Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing. Design/methodology/approach This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeerX data set and then evaluates the generated citation context lists by applying a user case study on 30 articles. Findings Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeerX using a list sorted by citation counts. Practical implications This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization. Originality/value The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

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Section: Research On Diversification For Re-rankingmentioning

confidence: 99%

Exploring multiple diversification strategies for academic citation contexts recommendation

Chen

Yang²,

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, users often cannot accurately describe the information they need, leading to the system returning a large number of unexpected results. One solution to the vocabulary problem is to increase the number of keywords in the query [3] . However, users tend to prefer using a small number of keywords for searching.…”

Section: Introductionmentioning

confidence: 99%