Re-ranking Answers by Discarding Biases in cQA Sites

• 1、School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876

Abstract：The vote mechanism employed to rank answers in community-based question answering websites is not very accurate because users will not vote to answers entirely base on their quality. Both the position and the appearance of an answer have an effect on the probability of users voting to it. Except the position bias and appearance bias, the following relationship between users impacts the voting results, too. As a result, the top answers obtained by vote mechanism is not reliable, especially when the votes is not sufficient. To rank answers based on their quality, this paper discussed the influences of the relationship between users to the vote mechanism and proposed a vote process model. Firstly, some assumptions about user's vote activities are made, then the vote process model is processed based on these assumptions to model user's voting process. Through the model inference process, the final equation to calculate answer's quality is get. Finally, an expectation-maximization algorithm is used to calculate the parameters in the final equation. By modeling user voting process,the vote process model can eliminate the influences of biases mentioned above and get the real quality evaluation of answers. Experiments on real dataset demonstrates the effectieness of the model proposed in this paper. In particular, when 30 percent of training data is used, the vote process model achieves a 10.1 percent improvement in precision and a 7.5 percent improvement in MRR compared with the joint click model, which is the state of the art click model.

keywords： data mining, cqa sites, social bias, rank answers

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