User interest modeling and its application for question recommendation in user-interactive question answering systems

Ni, Xingliang; Lu, Yao; Wenyin, Liu; Hua, Bin

doi:10.1016/j.ipm.2011.09.002

Cited by 20 publications

(13 citation statements)

References 6 publications

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“…The recommendations made in Q&A communities can be classified as answerer recommendation, question recommendation and answer recommendation [4]. In answerer recommendation, the approaches used to find potential answerers can be broadly divided into graph-based [18] and feature-based approaches [19].…”

Section: Related Workmentioning

confidence: 99%

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Incorporating social information to perform diverse replier recommendation in question and answer communities

Liu

Lin

Zheng

et al. 2015

Journal of Information Science

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Social information is contextual information that has made significant contributions to intelligent information systems. However, social information has not been fully used, especially in question and answer (Q&A) systems. This study describes a contextual recommendation method in which diverse repliers are recommended for new questions using incorporated social information in Q&A communities. We have mined multiple kinds of social information by analysing social behaviours and relations found in a Q&A community and proposed an algorithm to incorporate different social information in various social contexts to perform diverse repliers' recommendations. Recommendation diversity and social contexts have been considered and the properly used social information has been emphasized in this study. We conducted experiments using a dataset collected from the Stack Overflow website. The results demonstrate that different social information makes different contributions in promoting question answering, and incorporating social information properly could improve recommendation diversity and performance, which would then result in the promotion of satisfactory question solving.

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Section: Related Workmentioning

confidence: 99%

“…Information overload problems have proved to be major issues in Q&A systems [4]. Recommending suitable answerers to questions or recommending questions to interested users have been the main solutions that could promote question-answering but have a limited effect on promoting questioners satisfaction with questions solutions [5, 6].…”

Section: Introductionmentioning

confidence: 99%

Incorporating social information to perform diverse replier recommendation in question and answer communities

Liu

Lin

Zheng

et al. 2015

Journal of Information Science

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“…Guo et al introduced the User-Question-Answer model, which considers user profiles as topic mixtures, and uses question categories to improve the recommendation performance [8]. Ni et al proposed the Topic-based User Interest model, which leverages community selected best answers to promote users that contribute high quality posts [11].…”

Section: Related Workmentioning

confidence: 99%

“…A solution to this problem studied in previous work considers the task of automatically finding potential responders to questions [24,10,13,18,19,11,6,20,8]. These question recommendation systems aim at increasing participation by proactively warning users about the presence of questions suitable to their interests and expertise.…”

Section: Introductionmentioning

confidence: 99%

Question recommendation for collaborative question answering systems with RankSLDA

Pedro

Karatzoglou

2014

Proceedings of the 8th ACM Conference on Recommender Systems

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Collaborative question answering (CQA) communities rely on user participation for their success. This paper presents a supervised Bayesian approach to model expertise in online CQA communities with application to question recommendation, aimed at reducing waiting times for responses and avoiding question starvation. We propose a novel algorithm called RankSLDA which extends the supervised Latent Dirichlet Allocation model by considering a learningto-rank paradigm. This allows us to exploit the inherent collaborative effects that are present in CQA communities where users tend to answer questions in their topics of expertise. Users can thus be modeled on the basis of the topics in which they demonstrate expertise. In the supervised stage of the method we model the pairwise order of expertise of users on a given question. We compare RankSLDA against several alternative methods on data from the Cross Validate community, part of the Stack Exchange network. RankSLDA outperforms all alternative methods by a significant margin.

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“…Similar technique used in [8] is noticed in [9] and [10] as well with slight changes in the process. Ni et al [11] build a topic based user interest model to integrate with question answering. Though this research sounds well in the area of user based question answering, the model they propose is used only to recommend appropriate questions.…”

Section: Related Workmentioning

confidence: 99%

Interaction History Based Answer Formulation for Question Answering

Perera

Nand

2014

Knowledge Engineering and the Semantic Web

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Abstract. With the rapid growth in information access methodologies, question answering has drawn considerable attention among others. Though question answering has emerged as an interesting new research domain, still it is vastly concentrated on question processing and answer extraction approaches. Latter steps like answer ranking, formulation and presentations are not treated in depth. Weakness we found in this arena is that answers that a particular user has acquired are not considered, when processing new questions. As a result, current systems are not capable of linking two questions such as, "When is the Apple founded?" with a previously processed question "When is the Microsoft founded ?" generating an answer in the form of "Apple is founded one year later Microsoft founded, in 1976". In this paper we present an approach towards question answering to devise an answer based on the questions already processed by the system for a particular user which is termed as interaction history for the user. Our approach is a combination of question processing, relation extraction and knowledge representation with inference models. During the process we primarily focus on acquiring knowledge and building up a scalable user model to formulate future answers based on current answers that same user has processed. According to evaluation we carried out based on the TREC resources shows that proposed technology is promising and effective in question answering.

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User interest modeling and its application for question recommendation in user-interactive question answering systems

Cited by 20 publications

References 6 publications

Incorporating social information to perform diverse replier recommendation in question and answer communities

Incorporating social information to perform diverse replier recommendation in question and answer communities

Question recommendation for collaborative question answering systems with RankSLDA

Interaction History Based Answer Formulation for Question Answering

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