Community-based Question Answering (CQA) sites, such as Yahoo! Answers, Baidu Knows, Naver, and Quora, have been rapidly growing in popularity. The resulting archives of posted answers to questions, in Yahoo! Answers alone, already exceed in size 1 billion, and are aggressively indexed by web search engines. In fact, a large number of search engine users benefit from these archives, by finding existing answers that address their own queries. This scenario poses new challenges and opportunities for both search engines and CQA sites. To this end, we formulate a new problem of predicting the satisfaction of web searchers with CQA answers. We analyze a large number of web searches that result in a visit to a popular CQA site, and identify unique characteristics of searcher satisfaction in this setting, namely, the effects of query clarity, query-to-question match, and answer quality. We then propose and evaluate several approaches to predicting searcher satisfaction that exploit these characteristics. To the best of our knowledge, this is the first attempt to predict and validate the usefulness of CQA archives for external searchers, rather than for the original askers. Our results suggest promising directions for improving and exploiting community question answering services in pursuit of satisfying even more Web search queries.
Liu et al. REF defined a CQA query as a query that results in a visit to a CQA site.
Predicting web searcher satisfaction with existing community-based answers
SIGIR '11
Computer Science