Preference enhanced hybrid expertise retrieval system in community question answering services

Kundu, Dipankar; Pal, Rajat Kumar; Mandal, Deba Prasad

doi:10.1016/j.dss.2019.113164

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…They can be identified through analysing patterns of user activities on CQAs. [25,57,69] Reputation collectors A handful of users on CQAs are involved in diluting the quality of content and receiving a high reputation in the early phase. Such users are termed as reputation collectors.…”

Section: Duplicate Question Detectionmentioning

confidence: 99%

“…The proficiency of responses was used to assess an answerer's expertise. Kundu et al [69] used data derived from the social behaviour network, user profiles, and question and answer language. Liu et al [173] suggested a graph convolutional neural network-based model for expert prediction in the CQA website.…”

Section: Topical Expert Identificationmentioning

confidence: 99%

“…Kundu et al. [69] used data derived from the social behaviour network, user profiles, and question and answer language. Liu et al.…”

Section: User‐related Studies On Cqasmentioning

confidence: 99%

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Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review

Roy

Singh

Banerjee

et al. 2022

CAAI Trans on Intel Tech

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Over the last couple of decades, community question-answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and deep learning (DL) to explore the ever-growing volume of content that CQAs engender. To clarify the current state of the CQA literature that has used ML and DL, this paper reports a systematic literature review. The goal is to summarise and synthesise the major themes of CQA research related to (i) questions, (ii) answers and (iii) users. The final review included 133 articles. Dominant research themes include question quality, answer quality, and expert identification. In terms of dataset, some of the most widely studied platforms include Yahoo! Answers, Stack Exchange and Stack Overflow. The scope of most articles was confined to just one platform with few cross-platform investigations. Articles with ML outnumber those with DL. Nonetheless, the use of DL in CQA research is on an upward trajectory. A number of research directions are proposed.

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Section: Duplicate Question Detectionmentioning

confidence: 99%

Section: Topical Expert Identificationmentioning

confidence: 99%

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Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review

Roy

Singh

Banerjee

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Efforts include developing algorithms to calculate user authority in specific fields, using social network analysis, and the HITS and PageRank algorithms to identify experts [15,28,39,45,46]. Other studies have concentrated on reducing response wait times by finding similar questions or relevant answers within large archives by employing various information retrieval techniques [8,14,18,19,23,40]. Research has explored community dynamics like motivations for contribution and how user reputation impacts perceived answer quality [22,29].…”

Section: Related Work 51 Research On Qanda Platformsmentioning

confidence: 99%

“…𝑥) −𝛼 (1 + 𝑥) −𝛽 𝑑 𝑘 𝑑𝑥 𝑘 𝑥) 𝑘+𝛼 (1 + 𝑥) 𝑘+𝛽 (19). where 𝑘 is the order of the polynomial, 𝛼 and 𝛽 are parameters that shape the polynomial and weight function.…”

mentioning

confidence: 99%

Commercialization of state-owned broadcast networks in China

Yan¹

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Visual contents, such as illustrations and images, play a big role in product manual understanding. Existing Product Manual Question Answering (PMQA) datasets tend to ignore visual contents and only retain textual parts. In this work, to emphasize the importance of multimodal contents, we propose a Multimodal Product Manual Question Answering (MPMQA) task. For each question, MPMQA requires the model not only to process multimodal contents but also to provide multimodal answers. To support MPMQA, a large-scale dataset PM209 is constructed with human annotations, which contains 209 product manuals from 27 well-known consumer electronic brands. Human annotations include 6 types of semantic regions for manual contents and 22,021 pairs of question and answer. Especially, each answer consists of a textual sentence and related visual regions from manuals. Taking into account the length of product manuals and the fact that a question is always related to a small number of pages, MP-MQA can be naturally split into two subtasks: retrieving most related pages and then generating multimodal answers. We further propose a unified model that can perform these two subtasks all together and achieve comparable performance with multiple task-specific models. The PM209 dataset is available at https://github.com/AIM3-RUC/MPMQA.

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