{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:04:34Z","timestamp":1779203074430,"version":"3.51.4"},"reference-count":77,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T00:00:00Z","timestamp":1563926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"DST, India","award":["ECR\/2017\/00l691"],"award-info":[{"award-number":["ECR\/2017\/00l691"]}]},{"name":"Ramanujan Fellowship"},{"name":"Infosys Centre of AI, IIIT-Delhi, India"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2019,7,31]]},"abstract":"<jats:p>\n            Automatic estimation of relative difficulty of a pair of questions is an important and challenging problem in community question answering (CQA) services. There are limited studies that addressed this problem. Past studies mostly leveraged expertise of users answering the questions and barely considered other properties of CQA services such as metadata of users and posts, temporal information, and textual content. In this article, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem. DiffQue\u00a0starts by constructing a novel network structure that captures different notions of difficulties among a pair of questions. It then measures the relative difficulty of two questions by predicting the direction of a (virtual) edge connecting these two questions in the network. It leverages features extracted from the network structure, metadata of users\/posts, and textual description of questions and answers. Experiments on datasets obtained from two CQA sites (further divided into four datasets) with human annotated ground-truth show that DiffQue\u00a0outperforms four state-of-the-art methods by a significant margin (28.77% higher F\n            <jats:sub>1<\/jats:sub>\n            score and 28.72% higher AUC than the best baseline). As opposed to the other baselines, (i) DiffQue\u00a0appropriately responds to the training noise, (ii) DiffQue\u00a0is capable of adapting multiple domains (CQA datasets), and (iii) DiffQue\u00a0can efficiently handle the \u201ccold start\u201d problem that may arise due to the lack of information for newly posted questions or newly arrived users.\n          <\/jats:p>","DOI":"10.1145\/3337799","type":"journal-article","created":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T12:34:33Z","timestamp":1564058073000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["DiffQue"],"prefix":"10.1145","volume":"10","author":[{"given":"Deepak","family":"Thukral","sequence":"first","affiliation":[{"name":"Amazon Pvt. Ltd, Karnataka, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adesh","family":"Pandey","sequence":"additional","affiliation":[{"name":"Tower Research Capital India Pvt. 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