Re-Ranking Voting-Based Answers by Discarding User Behavior Biases

Xiaochi Wei, Heyan Huang, Chin-Yew Lin, Xin Xin, Xianling Mao, Shangguang Wang

Abstract: The vote mechanism is widely utilized to rank answers in community-based question answering sites. In generating a vote, a user's attention is influenced by the answer position and appearance, in addition to real answer quality. Previously, these biases are ignored. As a result, the top answers obtained from this mechanism are not reliable, if the number of votes for the active question is not sufficient. In this paper, we solve this problem by analyzing two kinds of biases; position bias and appearance bias. We identify the existence of these biases and propose a joint click model for dealing with both of them. Our experiments in real data demonstrate how the ranking performance of the proposed model outperforms traditional methods with biases ignored by 15:1% in [email protected], and 11:7% in the mean reciprocal rank. A case study on a manually labeled dataset futher supports the effectiveness of the proposed model.