Explanatory Q&A recommendation algorithm in community question answering

Abstract

PurposeIn community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.Design/methodology/approachIn the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.FindingsThe GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.Research limitations/implicationsThe proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.Originality/valueA novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

Personalized Art Work Recommendation System and Methods Based on User Interest Characteristics and Emotional Preferences

“…This algorithm exhibited good clustering performance, and the performance of the integrated classification model was superior to other algorithms. The high score of its Q&A recommendation performance indicated the practicality and good performance of the proposed recommendation algorithm, providing a new perspective for recommendation research [10]. Du et al used users' subjective characteristics and trust to improve similarity.…”

MSQ: a mobile and social-based Q&A system

“…In Li et al. (2020a, b), the authors solve the loading of manual Q&A system by providing a combined GNG clustering and classification model which improves the recommendation quality and accuracy effectiveness. ReferralWeb (Kautz et al.…”

“…Likewise, Li et al. (2020a, b) provides a two-stage survey to examine the ResearchGate Q&A system in terms of its answer qualities, and figures out providing opinions is the most criterion for providing better answer quality, followed by completeness and value-added criteria. Similarly, Li et al.…”