Recommending questions using the mdl-based tree cut model

Abstract

The paper is concerned with the problem of question recommendation. Specifically, given a question as query, we are to retrieve and rank other questions according to their likelihood of being good recommendations of the queried question. A good recommendation provides alternative aspects around users' interest. We tackle the problem of question recommendation in two steps: first represent questions as graphs of topic terms, and then rank recommendations on the basis of the graphs. We formalize both steps as the tree-cutting problems and then employ the MDL (Minimum Description Length) for selecting the best cuts. Experiments have been conducted with the real questions posted at Yahoo! Answers. The questions are about two domains, 'travel' and 'computers & internet'. Experimental results indicate that the use of the MDL-based tree cut model can significantly outperform the baseline methods of word-based VSM or phrasebased VSM. The results also show that the use of the MDL-based tree cut model is essential to our approach.

Cross-Language Question Re-Ranking

“…Cao et al [6] tackled it by comparing representations based on topic term graphs, i.e., by judging topic similarity and question focus. Jeon, Cro , and Lee [15] and Zhou et al [33] dodged the lexical gap between questions by assessing their similarity on the basis of a (monolingual) translation model.…”

Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

“…Typically, it has been addressed using a variety of textual similarity measures. Some work has paid attention to modeling the question topic, which can be done explicitly, e.g., using a graph of topic terms (Cao et al, 2008), or implicitly, e.g., using LDA-based topic language model that matches the questions not only at the term level but also at the topic level . Another important aspect is syntactic structure, e.g., Wang et al (2009) proposed a retrieval model for finding similar questions based on the similarity of syntactic trees, and Da San Martino et al (2016) used syntactic kernels.…”

Predicting Answering Behaviour in Online Question Answering Communities

“…Although question routing [22,15,23,10,12,11,6,17,9,5,20] and question recommendation [3,7] research aim at di↵erent problems, both approaches have similar methodologies. Question recommendation approaches can be easily used for question routing and vice versa.…”

“…However, rather than focusing on user decisions, such models focused on user reputation graphs [9] or topic hierarchies [3].…”

“…Stack Exchange, 1 Yahoo! Answers, 2 and Quora, 3 have witnessed a rapid increase in popularity in recent years as more and more people are going online to seek answers to their questions.…”